diff --git a/ConvBigru_IRPE_Attention特定数据集.ipynb b/ConvBigru_IRPE_Attention特定数据集.ipynb
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+++ b/ConvBigru_IRPE_Attention特定数据集.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt\n",
+    "from numpy import concatenate\n",
+    "from matplotlib import pyplot\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from tensorflow.keras import Sequential\n",
+    "\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.layers import LSTM\n",
+    "from tensorflow.keras.layers import Dropout\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这段代码是一个函数 time_series_to_supervised，它用于将时间序列数据转换为监督学习问题的数据集。下面是该函数的各个部分的含义：\n",
+    "\n",
+    "data: 输入的时间序列数据，可以是列表或2D NumPy数组。\n",
+    "n_in: 作为输入的滞后观察数，即用多少个时间步的观察值作为输入。默认值为96，表示使用前96个时间步的观察值作为输入。\n",
+    "n_out: 作为输出的观测数量，即预测多少个时间步的观察值。默认值为1，表示预测未来1个时间步的观察值。\n",
+    "dropnan: 布尔值，表示是否删除具有NaN值的行。默认为True，即删除具有NaN值的行。\n",
+    "函数首先检查输入数据的维度，并初始化一些变量。然后，它创建一个新的DataFrame对象 df 来存储输入数据，并保存原始的列名。接着，它创建了两个空列表 cols 和 names，用于存储新的特征列和列名。\n",
+    "\n",
+    "接下来，函数开始构建特征列和对应的列名。首先，它将原始的观察序列添加到 cols 列表中，并将其列名添加到 names 列表中。然后，它依次将滞后的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t-滞后时间)。这样就创建了输入特征的部分。\n",
+    "\n",
+    "接着，函数开始构建输出特征的部分。它依次将未来的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t+未来时间)。\n",
+    "\n",
+    "最后，函数将所有的特征列拼接在一起，构成一个新的DataFrame对象 agg。如果 dropnan 参数为True，则删除具有NaN值的行。最后，函数返回处理后的数据集 agg。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_series_to_supervised(data, n_in=96, n_out=1,dropnan=True):\n",
+    "    \"\"\"\n",
+    "    :param data:作为列表或2D NumPy数组的观察序列。需要。\n",
+    "    :param n_in:作为输入的滞后观察数（X）。值可以在[1..len（数据）]之间可选。默认为1。\n",
+    "    :param n_out:作为输出的观测数量（y）。值可以在[0..len（数据）]之间。可选的。默认为1。\n",
+    "    :param dropnan:Boolean是否删除具有NaN值的行。可选的。默认为True。\n",
+    "    :return:\n",
+    "    \"\"\"\n",
+    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
+    "    df = pd.DataFrame(data)\n",
+    "    origNames = df.columns\n",
+    "    cols, names = list(), list()\n",
+    "    cols.append(df.shift(0))\n",
+    "    names += [('%s' % origNames[j]) for j in range(n_vars)]\n",
+    "    n_in = max(0, n_in)\n",
+    "    for i in range(n_in, 0, -1):\n",
+    "        time = '(t-%d)' % i\n",
+    "        cols.append(df.shift(i))\n",
+    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
+    "    n_out = max(n_out, 0)\n",
+    "    for i in range(1, n_out+1):\n",
+    "        time = '(t+%d)' % i\n",
+    "        cols.append(df.shift(-i))\n",
+    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
+    "    agg = pd.concat(cols, axis=1)\n",
+    "    agg.columns = names\n",
+    "    if dropnan:\n",
+    "        agg.dropna(inplace=True)\n",
+    "    return agg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Temp   Humidity       GHI       DHI  Rainfall  Power\n",
+      "0  19.779453  40.025826  3.232706  1.690531       0.0    0.0\n",
+      "1  19.714937  39.605961  3.194991  1.576346       0.0    0.0\n",
+      "2  19.549330  39.608631  3.070866  1.576157       0.0    0.0\n",
+      "3  19.405870  39.680702  3.038623  1.482489       0.0    0.0\n",
+      "4  19.387363  39.319881  2.656474  1.134153       0.0    0.0\n",
+      "(104256, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 加载数据\n",
+    "path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\data6.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "datas1 = pd.DataFrame(pd.read_csv(path1))\n",
+    "#我只取了data表里的第3、23、16、17、18、19、20、21、27列，如果取全部列的话这一行可以去掉\n",
+    "# data1 = datas1.iloc[:,np.r_[3,23,16:22,27]]\n",
+    "data1=datas1.interpolate()\n",
+    "values1 = data1.values\n",
+    "print(data1.head())\n",
+    "print(data1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(104256, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 使用MinMaxScaler进行归一化\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "scaledData1 = scaler.fit_transform(data1)\n",
+    "print(scaledData1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            0         1         2         3    4         5   0(t-96)  \\\n",
+      "96   0.555631  0.349673  0.190042  0.040558  0.0  0.236302  0.490360   \n",
+      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.258204  0.489088   \n",
+      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.279860  0.485824   \n",
+      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.301336  0.482997   \n",
+      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.322851  0.482632   \n",
+      "\n",
+      "      1(t-96)   2(t-96)   3(t-96)  ...    2(t-1)    3(t-1)  4(t-1)    5(t-1)  \\\n",
+      "96   0.369105  0.002088  0.002013  ...  0.166009  0.036794     0.0  0.214129   \n",
+      "97   0.364859  0.002061  0.001839  ...  0.190042  0.040558     0.0  0.236302   \n",
+      "98   0.364886  0.001973  0.001839  ...  0.211335  0.044613     0.0  0.258204   \n",
+      "99   0.365615  0.001950  0.001697  ...  0.229657  0.047549     0.0  0.279860   \n",
+      "100  0.361965  0.001679  0.001167  ...  0.247775  0.053347     0.0  0.301336   \n",
+      "\n",
+      "       0(t+1)    1(t+1)    2(t+1)    3(t+1)  4(t+1)    5(t+1)  \n",
+      "96   0.564819  0.315350  0.211335  0.044613     0.0  0.258204  \n",
+      "97   0.576854  0.288321  0.229657  0.047549     0.0  0.279860  \n",
+      "98   0.581973  0.268243  0.247775  0.053347     0.0  0.301336  \n",
+      "99   0.586026  0.264586  0.266058  0.057351     0.0  0.322851  \n",
+      "100  0.590772  0.258790  0.282900  0.060958     0.0  0.343360  \n",
+      "\n",
+      "[5 rows x 588 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_steps_in =96 #历史时间长度\n",
+    "n_steps_out=1#预测时间长度\n",
+    "processedData1 = time_series_to_supervised(scaledData1,n_steps_in,n_steps_out)\n",
+    "print(processedData1.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']#去除power剩下的做标签列\n",
+    "data_y = processedData1.loc[:,'5']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "冒号\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104159, 576)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96        0.236302\n",
+       "97        0.258204\n",
+       "98        0.279860\n",
+       "99        0.301336\n",
+       "100       0.322851\n",
+       "            ...   \n",
+       "104250    0.000000\n",
+       "104251    0.000000\n",
+       "104252    0.000000\n",
+       "104253    0.000000\n",
+       "104254    0.000000\n",
+       "Name: 5, Length: 104159, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104159,)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(93743, 96, 6) (93743,) (8854, 96, 6) (8854,) (1562, 96, 6) (1562,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 计算训练集、验证集和测试集的大小\n",
+    "train_size = int(len(data_x) * 0.90)\n",
+    "test_size = int(len(data_x) * 0.015)\n",
+    "val_size = len(data_x) - train_size - test_size\n",
+    "\n",
+    "# 计算训练集、验证集和测试集的索引范围\n",
+    "train_indices = range(train_size)\n",
+    "val_indices = range(train_size, train_size + val_size)\n",
+    "test_indices = range(train_size + val_size, len(data_x))\n",
+    "\n",
+    "# 根据索引范围划分数据集\n",
+    "train_X1 = data_x.iloc[train_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
+    "val_X1 = data_x.iloc[val_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
+    "test_X1 = data_x.iloc[test_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
+    "train_y = data_y.iloc[train_indices].values\n",
+    "val_y = data_y.iloc[val_indices].values\n",
+    "test_y = data_y.iloc[test_indices].values\n",
+    "\n",
+    "# reshape input to be 3D [samples, timesteps, features]\n",
+    "train_X = train_X1.reshape((train_X1.shape[0], n_steps_in, scaledData1.shape[1]))\n",
+    "val_X = val_X1.reshape((val_X1.shape[0], n_steps_in, scaledData1.shape[1]))\n",
+    "test_X = test_X1.reshape((test_X1.shape[0], n_steps_in, scaledData1.shape[1]))\n",
+    "\n",
+    "print(train_X.shape, train_y.shape, val_X.shape, val_y.shape, test_X.shape, test_y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(93743, 96, 6)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_X1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_4\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_4\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_4       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv1d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">832</span> │ input_layer_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ max_pooling1d_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)      │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ bidirectional_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,920</span> │ max_pooling1d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ attention_with_imp… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>,     │     <span style=\"color: #00af00; text-decoration-color: #00af00\">66,304</span> │ bidirectional_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AttentionWithImpr…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>,   │            │ bidirectional_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
+       "│                     │ <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]      │            │ bidirectional_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ attention_with_i… │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │ global_average_p… │\n",
+       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_4       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m6\u001b[0m)     │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
+       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv1d_4 (\u001b[38;5;33mConv1D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │        \u001b[38;5;34m832\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m]… │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ max_pooling1d_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ conv1d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
+       "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m)      │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ bidirectional_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m49,920\u001b[0m │ max_pooling1d_4[\u001b[38;5;34m…\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ attention_with_imp… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m,     │     \u001b[38;5;34m66,304\u001b[0m │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
+       "│ (\u001b[38;5;33mAttentionWithImpr…\u001b[0m │ \u001b[38;5;34m128\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m,   │            │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
+       "│                     │ \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)]      │            │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ attention_with_i… │\n",
+       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_24 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │        \u001b[38;5;34m129\u001b[0m │ global_average_p… │\n",
+       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">117,185</span> (457.75 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m117,185\u001b[0m (457.75 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">117,185</span> (457.75 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m117,185\u001b[0m (457.75 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import Input, Conv1D, Bidirectional, GlobalAveragePooling1D, Dense, GRU, MaxPooling1D\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.initializers import RandomUniform\n",
+    "class AttentionWithImproveRelativePositionEncoding(tf.keras.layers.Layer):\n",
+    "    def __init__(self, d_model, num_heads, max_len=5000):\n",
+    "        super(AttentionWithImproveRelativePositionEncoding, self).__init__()\n",
+    "        self.num_heads = num_heads\n",
+    "        self.d_model = d_model\n",
+    "        self.max_len = max_len\n",
+    "        self.wq = tf.keras.layers.Dense(d_model)\n",
+    "        self.wk = tf.keras.layers.Dense(d_model)\n",
+    "        self.wv = tf.keras.layers.Dense(d_model)\n",
+    "        self.dense = tf.keras.layers.Dense(d_model)\n",
+    "        self.position_encoding = ImproveRelativePositionEncoding(d_model)\n",
+    "\n",
+    "    def call(self, v, k, q, mask):\n",
+    "        batch_size = tf.shape(q)[0]\n",
+    "        q = self.wq(q)\n",
+    "        k = self.wk(k)\n",
+    "        v = self.wv(v)\n",
+    "\n",
+    "        # 添加位置编码\n",
+    "        k += self.position_encoding (k)\n",
+    "        q += self.position_encoding (q)\n",
+    "\n",
+    "        q = self.split_heads(q, batch_size)\n",
+    "        k = self.split_heads(k, batch_size)\n",
+    "        v = self.split_heads(v, batch_size)\n",
+    "\n",
+    "        scaled_attention, attention_weights = self.scaled_dot_product_attention(q, k, v, mask)\n",
+    "        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])\n",
+    "        concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))\n",
+    "        output = self.dense(concat_attention)\n",
+    "        return output, attention_weights\n",
+    "\n",
+    "    def split_heads(self, x, batch_size):\n",
+    "        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.d_model // self.num_heads))\n",
+    "        return tf.transpose(x, perm=[0, 2, 1, 3])\n",
+    "\n",
+    "    def scaled_dot_product_attention(self, q, k, v, mask):\n",
+    "        matmul_qk = tf.matmul(q, k, transpose_b=True)\n",
+    "        dk = tf.cast(tf.shape(k)[-1], tf.float32)\n",
+    "        scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n",
+    "\n",
+    "        if mask is not None:\n",
+    "            scaled_attention_logits += (mask * -1e9)\n",
+    "\n",
+    "        attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)\n",
+    "        output = tf.matmul(attention_weights, v)\n",
+    "        return output, attention_weights\n",
+    "\n",
+    "class ImproveRelativePositionEncoding(tf.keras.layers.Layer):\n",
+    "    def __init__(self, d_model, max_len=5000):\n",
+    "        super(ImproveRelativePositionEncoding, self).__init__()\n",
+    "        self.max_len = max_len\n",
+    "        self.d_model = d_model\n",
+    "        # 引入可变化的参数u和v进行线性变化\n",
+    "        self.u = self.add_weight(shape=(self.d_model,),\n",
+    "                                 initializer=RandomUniform(),\n",
+    "                                 trainable=True)\n",
+    "        self.v = self.add_weight(shape=(self.d_model,),\n",
+    "                                 initializer=RandomUniform(),\n",
+    "                                 trainable=True)\n",
+    "    def call(self, inputs):\n",
+    "        seq_length = inputs.shape[1]\n",
+    "        pos_encoding = self.relative_positional_encoding(seq_length, self.d_model)\n",
+    "        \n",
+    "        # 调整原始的相对位置编码公式，将u和v参数融入其中\n",
+    "        pe_with_params = pos_encoding * self.u+ pos_encoding * self.v\n",
+    "        return inputs + pe_with_params\n",
+    "\n",
+    "    def relative_positional_encoding(self, position, d_model):\n",
+    "        pos = tf.range(position, dtype=tf.float32)\n",
+    "        i = tf.range(d_model, dtype=tf.float32)\n",
+    "        \n",
+    "        angles = 1 / tf.pow(10000.0, (2 * (i // 2)) / tf.cast(d_model, tf.float32))\n",
+    "        angle_rads = tf.einsum('i,j->ij', pos, angles)\n",
+    "        #保留了sinous机制\n",
+    "        # Apply sin to even indices; 2i\n",
+    "        angle_rads_sin = tf.sin(angle_rads[:, 0::2])\n",
+    "        # Apply cos to odd indices; 2i+1\n",
+    "        angle_rads_cos = tf.cos(angle_rads[:, 1::2])\n",
+    "\n",
+    "        pos_encoding = tf.stack([angle_rads_sin, angle_rads_cos], axis=2)\n",
+    "        pos_encoding = tf.reshape(pos_encoding, [1, position, d_model])\n",
+    "\n",
+    "        return pos_encoding\n",
+    "\n",
+    "\n",
+    "\n",
+    "def PosConv1biGRUWithSelfAttention(input_shape, gru_units, num_heads):\n",
+    "    inputs = Input(shape=input_shape)\n",
+    "    # CNN layer\n",
+    "    cnn_layer = Conv1D(filters=64, kernel_size=2, activation='relu')(inputs)\n",
+    "    cnn_layer = MaxPooling1D(pool_size=1)(cnn_layer)\n",
+    "    gru_output = Bidirectional(GRU(gru_units, return_sequences=True))(cnn_layer)\n",
+    "    \n",
+    "    # Apply Self-Attention\n",
+    "    self_attention =AttentionWithImproveRelativePositionEncoding(d_model=gru_units*2, num_heads=num_heads)\n",
+    "    gru_output, _ = self_attention(gru_output, gru_output, gru_output, mask=None)\n",
+    "    \n",
+    "    pool1 = GlobalAveragePooling1D()(gru_output)\n",
+    "    output = Dense(1)(pool1)\n",
+    "    \n",
+    "    return Model(inputs=inputs, outputs=output)\n",
+    "\n",
+    "\n",
+    "input_shape = (96, 6)\n",
+    "gru_units = 64\n",
+    "num_heads = 8\n",
+    "\n",
+    "# Create model\n",
+    "model = PosConv1biGRUWithSelfAttention(input_shape, gru_units, num_heads)\n",
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 71ms/step - loss: 0.0198 - val_loss: 0.0016\n",
+      "Epoch 2/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 73ms/step - loss: 0.0016 - val_loss: 0.0015\n",
+      "Epoch 3/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 74ms/step - loss: 0.0015 - val_loss: 0.0015\n",
+      "Epoch 4/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 73ms/step - loss: 0.0015 - val_loss: 0.0014\n",
+      "Epoch 5/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 73ms/step - loss: 0.0014 - val_loss: 0.0016\n",
+      "Epoch 6/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 71ms/step - loss: 0.0014 - val_loss: 0.0015\n",
+      "Epoch 7/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 71ms/step - loss: 0.0014 - val_loss: 0.0014\n",
+      "Epoch 8/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 73ms/step - loss: 0.0013 - val_loss: 0.0014\n",
+      "Epoch 9/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 73ms/step - loss: 0.0013 - val_loss: 0.0014\n",
+      "Epoch 10/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 72ms/step - loss: 0.0013 - val_loss: 0.0015\n",
+      "Epoch 11/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 71ms/step - loss: 0.0013 - val_loss: 0.0014\n",
+      "Epoch 12/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 72ms/step - loss: 0.0013 - val_loss: 0.0015\n",
+      "Epoch 13/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 73ms/step - loss: 0.0013 - val_loss: 0.0014\n",
+      "Epoch 14/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 74ms/step - loss: 0.0012 - val_loss: 0.0014\n",
+      "Epoch 15/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 73ms/step - loss: 0.0013 - val_loss: 0.0014\n",
+      "Epoch 16/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 71ms/step - loss: 0.0013 - val_loss: 0.0013\n",
+      "Epoch 17/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 72ms/step - loss: 0.0013 - val_loss: 0.0013\n",
+      "Epoch 18/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 72ms/step - loss: 0.0012 - val_loss: 0.0014\n",
+      "Epoch 19/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 69ms/step - loss: 0.0012 - val_loss: 0.0013\n",
+      "Epoch 20/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 70ms/step - loss: 0.0012 - val_loss: 0.0014\n",
+      "Epoch 21/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 70ms/step - loss: 0.0012 - val_loss: 0.0014\n",
+      "Epoch 22/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 70ms/step - loss: 0.0011 - val_loss: 0.0014\n",
+      "Epoch 23/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 69ms/step - loss: 0.0012 - val_loss: 0.0018\n",
+      "Epoch 24/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 69ms/step - loss: 0.0012 - val_loss: 0.0014\n",
+      "Epoch 25/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 70ms/step - loss: 0.0011 - val_loss: 0.0014\n",
+      "Epoch 26/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 70ms/step - loss: 0.0012 - val_loss: 0.0015\n",
+      "Epoch 27/100\n",
+      "\u001b[1m1465/1465\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 66ms/step - loss: 0.0012 - val_loss: 0.0015\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compile and train the model\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "\n",
+    "# 定义早停机制\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min')\n",
+    "\n",
+    "# 拟合模型，并添加早停机制和模型检查点\n",
+    "history = model.fit(train_X, train_y, epochs=100, batch_size=64, validation_data=(val_X, val_y), \n",
+    "                    callbacks=[early_stopping])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 预测\n",
+    "lstm_pred = model.predict(test_X)\n",
+    "# 将预测结果的形状修改为与原始数据相同的形状\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y_pre=test_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(history.history['loss'], label='train')\n",
+    "plt.plot(history.history['val_loss'], label='test')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1562, 1)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lstm_pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1562,)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_y_pre.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y_pre1=test_y_pre.reshape(1562,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.90540195],\n",
+       "       [0.90466702],\n",
+       "       [0.89696645],\n",
+       "       ...,\n",
+       "       [0.        ],\n",
+       "       [0.        ],\n",
+       "       [0.        ]])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_y_pre1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1 = np.broadcast_to(lstm_pred, (1562, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y2 = np.broadcast_to(test_y_pre1, (1562, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 反归一化\n",
+    "inv_forecast_y = scaler.inverse_transform(results1)\n",
+    "inv_test_y = scaler.inverse_transform(test_y2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 4.08374339e+01,  9.30529669e+01,  1.27546074e+03,\n",
+       "         5.95908965e+02,  2.15485743e+01,  4.67959929e+00],\n",
+       "       [ 4.08001461e+01,  9.29803001e+01,  1.27442567e+03,\n",
+       "         5.95425556e+02,  2.15310831e+01,  4.67580080e+00],\n",
+       "       [ 4.04094426e+01,  9.22188951e+01,  1.26358018e+03,\n",
+       "         5.90360385e+02,  2.13478095e+01,  4.63600016e+00],\n",
+       "       ...,\n",
+       "       [-5.09990072e+00,  3.53003502e+00,  2.91584611e-01,\n",
+       "         3.66558254e-01,  0.00000000e+00,  0.00000000e+00],\n",
+       "       [-5.09990072e+00,  3.53003502e+00,  2.91584611e-01,\n",
+       "         3.66558254e-01,  0.00000000e+00,  0.00000000e+00],\n",
+       "       [-5.09990072e+00,  3.53003502e+00,  2.91584611e-01,\n",
+       "         3.66558254e-01,  0.00000000e+00,  0.00000000e+00]])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inv_test_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test RMSE: 0.063\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 计算均方根误差\n",
+    "rmse = sqrt(mean_squared_error(inv_test_y[:,5], inv_forecast_y[:,5]))\n",
+    "print('Test RMSE: %.3f' % rmse)\n",
+    "#画图\n",
+    "plt.figure(figsize=(16,8))\n",
+    "plt.plot(inv_test_y[:,5], label='true')\n",
+    "plt.plot(inv_forecast_y[:,5], label='pre')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_squared_error: 0.00014629570256978046\n",
+      "mean_absolute_error: 0.008445659571024366\n",
+      "rmse: 0.01209527604355438\n",
+      "r2 score: 0.9988370101682903\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
+    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score\n",
+    "print('mean_squared_error:', mean_squared_error(lstm_pred, test_y_pre))  # mse)\n",
+    "print(\"mean_absolute_error:\", mean_absolute_error(lstm_pred, test_y_pre))  # mae\n",
+    "print(\"rmse:\", sqrt(mean_squared_error(lstm_pred,test_y_pre)))\n",
+    "#r2对比区域\n",
+    "print(\"r2 score:\", r2_score(inv_test_y[:], inv_forecast_y[:]))#预测50天数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df1.to_csv('test.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame(inv_forecast_y[:], columns=['column_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df2.to_csv('forecast.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/iceemdan-low-LSTM.ipynb b/iceemdan-low-LSTM.ipynb
new file mode 100644
index 0000000..1fbb803
--- /dev/null
+++ b/iceemdan-low-LSTM.ipynb
@@ -0,0 +1,1060 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
+      "C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
+      "  from pandas.core import (\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import sqrt\n",
+    "from numpy import concatenate\n",
+    "from matplotlib import pyplot\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from tensorflow.keras import Sequential\n",
+    "\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.layers import LSTM\n",
+    "from tensorflow.keras.layers import Dropout\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这段代码是一个函数 time_series_to_supervised，它用于将时间序列数据转换为监督学习问题的数据集。下面是该函数的各个部分的含义：\n",
+    "\n",
+    "data: 输入的时间序列数据，可以是列表或2D NumPy数组。\n",
+    "n_in: 作为输入的滞后观察数，即用多少个时间步的观察值作为输入。默认值为96，表示使用前96个时间步的观察值作为输入。\n",
+    "n_out: 作为输出的观测数量，即预测多少个时间步的观察值。默认值为10，表示预测未来10个时间步的观察值。\n",
+    "dropnan: 布尔值，表示是否删除具有NaN值的行。默认为True，即删除具有NaN值的行。\n",
+    "函数首先检查输入数据的维度，并初始化一些变量。然后，它创建一个新的DataFrame对象 df 来存储输入数据，并保存原始的列名。接着，它创建了两个空列表 cols 和 names，用于存储新的特征列和列名。\n",
+    "\n",
+    "接下来，函数开始构建特征列和对应的列名。首先，它将原始的观察序列添加到 cols 列表中，并将其列名添加到 names 列表中。然后，它依次将滞后的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t-滞后时间)。这样就创建了输入特征的部分。\n",
+    "\n",
+    "接着，函数开始构建输出特征的部分。它依次将未来的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t+未来时间)。\n",
+    "\n",
+    "最后，函数将所有的特征列拼接在一起，构成一个新的DataFrame对象 agg。如果 dropnan 参数为True，则删除具有NaN值的行。最后，函数返回处理后的数据集 agg。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_series_to_supervised(data, n_in=96, n_out=10,dropnan=True):\n",
+    "    \"\"\"\n",
+    "    :param data:作为列表或2D NumPy数组的观察序列。需要。\n",
+    "    :param n_in:作为输入的滞后观察数（X）。值可以在[1..len（数据）]之间可选。默认为1。\n",
+    "    :param n_out:作为输出的观测数量（y）。值可以在[0..len（数据）]之间。可选的。默认为1。\n",
+    "    :param dropnan:Boolean是否删除具有NaN值的行。可选的。默认为True。\n",
+    "    :return:\n",
+    "    \"\"\"\n",
+    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
+    "    df = pd.DataFrame(data)\n",
+    "    origNames = df.columns\n",
+    "    cols, names = list(), list()\n",
+    "    cols.append(df.shift(0))\n",
+    "    names += [('%s' % origNames[j]) for j in range(n_vars)]\n",
+    "    n_in = max(0, n_in)\n",
+    "    for i in range(n_in, 0, -1):\n",
+    "        time = '(t-%d)' % i\n",
+    "        cols.append(df.shift(i))\n",
+    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
+    "    n_out = max(n_out, 0)\n",
+    "    for i in range(1, n_out+1):\n",
+    "        time = '(t+%d)' % i\n",
+    "        cols.append(df.shift(-i))\n",
+    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
+    "    agg = pd.concat(cols, axis=1)\n",
+    "    agg.columns = names\n",
+    "    if dropnan:\n",
+    "        agg.dropna(inplace=True)\n",
+    "    return agg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Temp   Humidity       GHI       DHI  Rainfall  Power\n",
+      "0  19.779453  40.025826  3.232706  1.690531       0.0    0.0\n",
+      "1  19.714937  39.605961  3.194991  1.576346       0.0    0.0\n",
+      "2  19.549330  39.608631  3.070866  1.576157       0.0    0.0\n",
+      "3  19.405870  39.680702  3.038623  1.482489       0.0    0.0\n",
+      "4  19.387363  39.319881  2.656474  1.134153       0.0    0.0\n",
+      "(104256, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 加载数据\n",
+    "path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\data6.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "datas1 = pd.DataFrame(pd.read_csv(path1))\n",
+    "#我只取了data表里的第3、23、16、17、18、19、20、21、27列，如果取全部列的话这一行可以去掉\n",
+    "# data1 = datas1.iloc[:,np.r_[3,23,16:22,27]]\n",
+    "data1=datas1.interpolate()\n",
+    "values1 = data1.values\n",
+    "print(data1.head())\n",
+    "print(data1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data2= data1.drop(['date'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 获取重构的原始数据\n",
+    "# # 获取重构的原始数据\n",
+    "# # 获取重构的原始数据\n",
+    "path_re = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\iceemdan_reconstructed_data_low.csv\"#数据所在路径\n",
+    "# #我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "data_re = pd.DataFrame(pd.read_csv(path_re))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "</div>"
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+      "text/plain": [
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+       "3          1.426812\n",
+       "4          1.426810\n",
+       "...             ...\n",
+       "104251     1.629381\n",
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+       "104254     1.629213\n",
+       "104255     1.629152\n",
+       "\n",
+       "[104256 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# # 假设你已经有了原始数据和重构数据\n",
+    "# # 原始数据\n",
+    "original_data = data1['Power'].values\n",
+    "\n",
+    "# # 创建时间序列（假设时间序列与数据对应）\n",
+    "time = range(len(original_data))\n",
+    "\n",
+    "# # 创建画布和子图\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# # 绘制原始数据\n",
+    "# plt.plot(time, original_data, label='Original Data', color='blue')\n",
+    "\n",
+    "# # 绘制重构数据\n",
+    "plt.plot( data_re[:], label='Reconstructed Data', color='red')\n",
+    "\n",
+    "# # 添加标题和标签\n",
+    "plt.title('Comparison between Original and reconstructed_data_high')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Power')\n",
+    "plt.legend()\n",
+    "\n",
+    "# # 显示图形\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data3=data1.iloc[:,:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Temp   Humidity       GHI       DHI  Rainfall  column_name\n",
+      "0       19.779453  40.025826  3.232706  1.690531       0.0     1.426824\n",
+      "1       19.714937  39.605961  3.194991  1.576346       0.0     1.426819\n",
+      "2       19.549330  39.608631  3.070866  1.576157       0.0     1.426815\n",
+      "3       19.405870  39.680702  3.038623  1.482489       0.0     1.426812\n",
+      "4       19.387363  39.319881  2.656474  1.134153       0.0     1.426810\n",
+      "...           ...        ...       ...       ...       ...          ...\n",
+      "104251  13.303740  34.212711  1.210789  0.787026       0.0     1.629381\n",
+      "104252  13.120920  34.394939  2.142980  1.582670       0.0     1.629328\n",
+      "104253  12.879215  35.167400  1.926214  1.545889       0.0     1.629271\n",
+      "104254  12.915867  35.359989  1.317695  0.851529       0.0     1.629213\n",
+      "104255  13.134816  34.500034  1.043269  0.597816       0.0     1.629152\n",
+      "\n",
+      "[104256 rows x 6 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# # 创建data3和imf1_array对应的DataFrame\n",
+    "data3_df = pd.DataFrame(data3)\n",
+    "imf1_df = pd.DataFrame(data_re)\n",
+    "\n",
+    "# # 合并data3_df和imf1_df\n",
+    "merged_df = pd.concat([data3_df, imf1_df], axis=1)\n",
+    "\n",
+    "# # 设置行数为35040行\n",
+    "merged_df = merged_df.iloc[:104256]\n",
+    "\n",
+    "# # 打印合并后的表\n",
+    "print(merged_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104256, 6)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(104256, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 使用MinMaxScaler进行归一化\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "scaledData1 = scaler.fit_transform(merged_df)\n",
+    "print(scaledData1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            0         1         2         3    4         5   0(t-96)  \\\n",
+      "96   0.555631  0.349673  0.190042  0.040558  0.0  0.836699  0.490360   \n",
+      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.836762  0.489088   \n",
+      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.836826  0.485824   \n",
+      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.836891  0.482997   \n",
+      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.836956  0.482632   \n",
+      "\n",
+      "      1(t-96)   2(t-96)   3(t-96)  ...    2(t-1)    3(t-1)  4(t-1)    5(t-1)  \\\n",
+      "96   0.369105  0.002088  0.002013  ...  0.166009  0.036794     0.0  0.836635   \n",
+      "97   0.364859  0.002061  0.001839  ...  0.190042  0.040558     0.0  0.836699   \n",
+      "98   0.364886  0.001973  0.001839  ...  0.211335  0.044613     0.0  0.836762   \n",
+      "99   0.365615  0.001950  0.001697  ...  0.229657  0.047549     0.0  0.836826   \n",
+      "100  0.361965  0.001679  0.001167  ...  0.247775  0.053347     0.0  0.836891   \n",
+      "\n",
+      "       0(t+1)    1(t+1)    2(t+1)    3(t+1)  4(t+1)    5(t+1)  \n",
+      "96   0.564819  0.315350  0.211335  0.044613     0.0  0.836762  \n",
+      "97   0.576854  0.288321  0.229657  0.047549     0.0  0.836826  \n",
+      "98   0.581973  0.268243  0.247775  0.053347     0.0  0.836891  \n",
+      "99   0.586026  0.264586  0.266058  0.057351     0.0  0.836956  \n",
+      "100  0.590772  0.258790  0.282900  0.060958     0.0  0.837022  \n",
+      "\n",
+      "[5 rows x 588 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_steps_in =96 #历史时间长度\n",
+    "n_steps_out=1#预测时间长度\n",
+    "processedData1 = time_series_to_supervised(scaledData1,n_steps_in,n_steps_out)\n",
+    "print(processedData1.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# processedData1.to_csv('processedData1.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']\n",
+    "data_y = processedData1.loc[:,'5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104159, 576)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96        0.836699\n",
+       "97        0.836762\n",
+       "98        0.836826\n",
+       "99        0.836891\n",
+       "100       0.836956\n",
+       "            ...   \n",
+       "104250    0.989547\n",
+       "104251    0.989508\n",
+       "104252    0.989466\n",
+       "104253    0.989423\n",
+       "104254    0.989378\n",
+       "Name: 5, Length: 104159, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104159,)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(83328, 96, 6) (83328,) (20831, 96, 6) (20831,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 7.划分训练集和测试集\n",
+    "\n",
+    "test_size = int(len(data_x) * 0.2)\n",
+    "# 计算训练集和测试集的索引范围\n",
+    "train_indices = range(len(data_x) - test_size)\n",
+    "test_indices = range(len(data_x) - test_size, len(data_x))\n",
+    "\n",
+    "# 根据索引范围划分数据集\n",
+    "train_X1 = data_x.iloc[train_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
+    "test_X1 = data_x.iloc[test_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
+    "train_y = data_y.iloc[train_indices].values\n",
+    "test_y = data_y.iloc[test_indices].values\n",
+    "\n",
+    "\n",
+    "# # 多次运行代码时希望得到相同的数据分割，可以设置 random_state 参数为一个固定的整数值\n",
+    "# train_X1,test_X1, train_y, test_y = train_test_split(data_x.values, data_y.values, test_size=0.2, random_state=343)\n",
+    "# reshape input to be 3D [samples, timesteps, features]\n",
+    "train_X = train_X1.reshape((train_X1.shape[0], n_steps_in, scaledData1.shape[1]))\n",
+    "test_X = test_X1.reshape((test_X1.shape[0], n_steps_in,scaledData1.shape[1]))\n",
+    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)\n",
+    "# 使用train_test_split函数划分训练集和测试集，测试集的比重是40%。\n",
+    "# 然后将train_X1、test_X1进行一个升维，变成三维，维数分别是[samples,timesteps,features]。\n",
+    "# 打印一下他们的shape：\\\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(83328, 96, 6)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_X1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\Anaconda3\\lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">69,120</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m69,120\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,249</span> (270.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m69,249\u001b[0m (270.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,249</span> (270.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m69,249\u001b[0m (270.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from keras.layers import GRU, Bidirectional\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input, Conv1D, MaxPooling1D, LSTM, Dense, Attention, Flatten\n",
+    "import keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import LSTM, Dense\n",
+    "\n",
+    "# 创建模型\n",
+    "model = Sequential()\n",
+    "\n",
+    "# 添加单层 LSTM\n",
+    "model.add(LSTM(units=128, input_shape=(96, 6)))\n",
+    "\n",
+    "# 添加输出层\n",
+    "model.add(Dense(1))\n",
+    "\n",
+    "# 编译模型\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "# 查看模型结构\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m122s\u001b[0m 92ms/step - loss: 0.0156 - val_loss: 1.0318e-05\n",
+      "Epoch 2/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 85ms/step - loss: 1.2280e-05 - val_loss: 2.9811e-06\n",
+      "Epoch 3/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 87ms/step - loss: 9.1935e-06 - val_loss: 2.5579e-06\n",
+      "Epoch 4/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.0443e-05 - val_loss: 8.4623e-06\n",
+      "Epoch 5/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 90ms/step - loss: 1.1108e-05 - val_loss: 8.1167e-06\n",
+      "Epoch 6/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 85ms/step - loss: 5.3451e-06 - val_loss: 2.4689e-06\n",
+      "Epoch 7/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 90ms/step - loss: 1.5962e-05 - val_loss: 2.2134e-06\n",
+      "Epoch 8/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 5.3290e-06 - val_loss: 3.5285e-07\n",
+      "Epoch 9/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 97ms/step - loss: 4.5184e-06 - val_loss: 1.2596e-07\n",
+      "Epoch 10/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.6976e-06 - val_loss: 7.1095e-06\n",
+      "Epoch 11/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m123s\u001b[0m 95ms/step - loss: 6.6386e-06 - val_loss: 1.0392e-07\n",
+      "Epoch 12/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 97ms/step - loss: 2.3165e-06 - val_loss: 8.4822e-07\n",
+      "Epoch 13/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 92ms/step - loss: 3.5823e-06 - val_loss: 4.9285e-08\n",
+      "Epoch 14/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 90ms/step - loss: 3.1791e-06 - val_loss: 2.2294e-07\n",
+      "Epoch 15/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 2.9977e-06 - val_loss: 3.9852e-06\n",
+      "Epoch 16/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 2.3874e-06 - val_loss: 1.3594e-07\n",
+      "Epoch 17/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 103ms/step - loss: 3.1801e-07 - val_loss: 1.6932e-07\n",
+      "Epoch 18/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.5647e-06 - val_loss: 2.1397e-08\n",
+      "Epoch 19/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 104ms/step - loss: 1.4188e-06 - val_loss: 1.4569e-07\n",
+      "Epoch 20/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 99ms/step - loss: 1.1043e-06 - val_loss: 5.9704e-07\n",
+      "Epoch 21/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 103ms/step - loss: 2.0067e-06 - val_loss: 2.0218e-06\n",
+      "Epoch 22/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 111ms/step - loss: 1.9982e-06 - val_loss: 2.2618e-07\n",
+      "Epoch 23/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 104ms/step - loss: 1.4178e-06 - val_loss: 1.3009e-06\n",
+      "Epoch 24/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 100ms/step - loss: 2.7170e-06 - val_loss: 1.2247e-08\n",
+      "Epoch 25/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 1.8664e-06 - val_loss: 5.6499e-07\n",
+      "Epoch 26/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 68ms/step - loss: 1.3434e-06 - val_loss: 1.2509e-08\n",
+      "Epoch 27/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 65ms/step - loss: 1.8632e-06 - val_loss: 5.3179e-07\n",
+      "Epoch 28/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 63ms/step - loss: 1.2746e-06 - val_loss: 9.0354e-08\n",
+      "Epoch 29/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 63ms/step - loss: 1.5440e-06 - val_loss: 1.2604e-07\n",
+      "Epoch 30/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 68ms/step - loss: 1.2646e-06 - val_loss: 2.5639e-07\n",
+      "Epoch 31/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 62ms/step - loss: 1.3377e-06 - val_loss: 4.0479e-08\n",
+      "Epoch 32/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 37ms/step - loss: 7.9140e-07 - val_loss: 1.1824e-06\n",
+      "Epoch 33/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 37ms/step - loss: 2.1865e-06 - val_loss: 4.2140e-06\n",
+      "Epoch 34/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 39ms/step - loss: 1.4884e-06 - val_loss: 1.8359e-06\n",
+      "\u001b[1m651/651\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compile and train the model\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "\n",
+    "# 定义早停机制\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min')\n",
+    "\n",
+    "# 拟合模型，并添加早停机制和模型检查点\n",
+    "history = model.fit(train_X, train_y, epochs=100, batch_size=64, validation_data=(test_X, test_y), \n",
+    "                    callbacks=[early_stopping])\n",
+    "# 预测\n",
+    "lstm_pred = model.predict(test_X)\n",
+    "# 将预测结果的形状修改为与原始数据相同的形状"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20831, 1)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lstm_pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20831,)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y1=test_y.reshape(20831,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.65620206],\n",
+       "       [0.6565139 ],\n",
+       "       [0.65682633],\n",
+       "       ...,\n",
+       "       [0.98946626],\n",
+       "       [0.98942303],\n",
+       "       [0.98937795]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_y1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1 = np.broadcast_to(lstm_pred, (20831, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y2 = np.broadcast_to(test_y1, (20831, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 反归一化\n",
+    "inv_forecast_y = scaler.inverse_transform(results1)\n",
+    "inv_test_y = scaler.inverse_transform(test_y2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2.81937911e+01, 6.84129659e+01, 9.24487326e+02, 4.31993807e+02,\n",
+       "        1.56176147e+01, 1.19628092e+00],\n",
+       "       [2.82096130e+01, 6.84437997e+01, 9.24926524e+02, 4.32198926e+02,\n",
+       "        1.56250366e+01, 1.19668613e+00],\n",
+       "       [2.82254649e+01, 6.84746920e+01, 9.25366555e+02, 4.32404434e+02,\n",
+       "        1.56324725e+01, 1.19709211e+00],\n",
+       "       ...,\n",
+       "       [4.51026009e+01, 1.01364948e+02, 1.39385702e+03, 6.51203592e+02,\n",
+       "        2.35493057e+01, 1.62932764e+00],\n",
+       "       [4.51004072e+01, 1.01360673e+02, 1.39379613e+03, 6.51175153e+02,\n",
+       "        2.35482767e+01, 1.62927146e+00],\n",
+       "       [4.50981204e+01, 1.01356216e+02, 1.39373265e+03, 6.51145506e+02,\n",
+       "        2.35472040e+01, 1.62921289e+00]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inv_test_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test RMSE: 0.002\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 计算均方根误差\n",
+    "rmse = sqrt(mean_squared_error(inv_test_y[:,5], inv_forecast_y[:,5]))\n",
+    "print('Test RMSE: %.3f' % rmse)\n",
+    "#画图\n",
+    "plt.figure(figsize=(16,8))\n",
+    "plt.plot(inv_test_y[:,5], label='true')\n",
+    "plt.plot(inv_forecast_y[:,5], label='pre')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_squared_error: 1.8358609523038586e-06\n",
+      "mean_absolute_error: 0.0012240899816947145\n",
+      "rmse: 0.0013549394644425479\n",
+      "r2 score: 0.9998451201868883\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
+    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score\n",
+    "print('mean_squared_error:', mean_squared_error(lstm_pred, test_y))  # mse)\n",
+    "print(\"mean_absolute_error:\", mean_absolute_error(lstm_pred, test_y))  # mae\n",
+    "print(\"rmse:\", sqrt(mean_squared_error(lstm_pred,test_y)))\n",
+    "print(\"r2 score:\", r2_score(inv_test_y[:], inv_forecast_y[:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df1.to_csv('低频_test.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame(inv_forecast_y[:,5], columns=['column_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df2.to_csv('低频_forecast.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/iceemdan-筛选-high-ConvBiGruAttention copy 2.ipynb b/iceemdan-筛选-high-ConvBiGruAttention copy 2.ipynb
new file mode 100644
index 0000000..ef894bb
--- /dev/null
+++ b/iceemdan-筛选-high-ConvBiGruAttention copy 2.ipynb	
@@ -0,0 +1,1102 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
+      "C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
+      "  from pandas.core import (\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import sqrt\n",
+    "from numpy import concatenate\n",
+    "from matplotlib import pyplot\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from tensorflow.keras import Sequential\n",
+    "\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.layers import LSTM\n",
+    "from tensorflow.keras.layers import Dropout\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这段代码是一个函数 time_series_to_supervised，它用于将时间序列数据转换为监督学习问题的数据集。下面是该函数的各个部分的含义：\n",
+    "\n",
+    "data: 输入的时间序列数据，可以是列表或2D NumPy数组。\n",
+    "n_in: 作为输入的滞后观察数，即用多少个时间步的观察值作为输入。默认值为96，表示使用前96个时间步的观察值作为输入。\n",
+    "n_out: 作为输出的观测数量，即预测多少个时间步的观察值。默认值为10，表示预测未来10个时间步的观察值。\n",
+    "dropnan: 布尔值，表示是否删除具有NaN值的行。默认为True，即删除具有NaN值的行。\n",
+    "函数首先检查输入数据的维度，并初始化一些变量。然后，它创建一个新的DataFrame对象 df 来存储输入数据，并保存原始的列名。接着，它创建了两个空列表 cols 和 names，用于存储新的特征列和列名。\n",
+    "\n",
+    "接下来，函数开始构建特征列和对应的列名。首先，它将原始的观察序列添加到 cols 列表中，并将其列名添加到 names 列表中。然后，它依次将滞后的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t-滞后时间)。这样就创建了输入特征的部分。\n",
+    "\n",
+    "接着，函数开始构建输出特征的部分。它依次将未来的观察序列添加到 cols 列表中，并构建相应的列名，格式为 (原始列名)(t+未来时间)。\n",
+    "\n",
+    "最后，函数将所有的特征列拼接在一起，构成一个新的DataFrame对象 agg。如果 dropnan 参数为True，则删除具有NaN值的行。最后，函数返回处理后的数据集 agg。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_series_to_supervised(data, n_in=96, n_out=10,dropnan=True):\n",
+    "    \"\"\"\n",
+    "    :param data:作为列表或2D NumPy数组的观察序列。需要。\n",
+    "    :param n_in:作为输入的滞后观察数（X）。值可以在[1..len（数据）]之间可选。默认为1。\n",
+    "    :param n_out:作为输出的观测数量（y）。值可以在[0..len（数据）]之间。可选的。默认为1。\n",
+    "    :param dropnan:Boolean是否删除具有NaN值的行。可选的。默认为True。\n",
+    "    :return:\n",
+    "    \"\"\"\n",
+    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
+    "    df = pd.DataFrame(data)\n",
+    "    origNames = df.columns\n",
+    "    cols, names = list(), list()\n",
+    "    cols.append(df.shift(0))\n",
+    "    names += [('%s' % origNames[j]) for j in range(n_vars)]\n",
+    "    n_in = max(0, n_in)\n",
+    "    for i in range(n_in, 0, -1):\n",
+    "        time = '(t-%d)' % i\n",
+    "        cols.append(df.shift(i))\n",
+    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
+    "    n_out = max(n_out, 0)\n",
+    "    for i in range(1, n_out+1):\n",
+    "        time = '(t+%d)' % i\n",
+    "        cols.append(df.shift(-i))\n",
+    "        names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]\n",
+    "    agg = pd.concat(cols, axis=1)\n",
+    "    agg.columns = names\n",
+    "    if dropnan:\n",
+    "        agg.dropna(inplace=True)\n",
+    "    return agg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Temp   Humidity       GHI       DHI  Rainfall  Power\n",
+      "0  19.779453  40.025826  3.232706  1.690531       0.0    0.0\n",
+      "1  19.714937  39.605961  3.194991  1.576346       0.0    0.0\n",
+      "2  19.549330  39.608631  3.070866  1.576157       0.0    0.0\n",
+      "3  19.405870  39.680702  3.038623  1.482489       0.0    0.0\n",
+      "4  19.387363  39.319881  2.656474  1.134153       0.0    0.0\n",
+      "(104256, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 加载数据\n",
+    "path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\data6.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "datas1 = pd.DataFrame(pd.read_csv(path1))\n",
+    "#我只取了data表里的第3、23、16、17、18、19、20、21、27列，如果取全部列的话这一行可以去掉\n",
+    "# data1 = datas1.iloc[:,np.r_[3,23,16:22,27]]\n",
+    "data1=datas1.interpolate()\n",
+    "values1 = data1.values\n",
+    "print(data1.head())\n",
+    "print(data1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data2= data1.drop(['date','Air_P','RH'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 获取重构的原始数据\n",
+    "# # 获取重构的原始数据\n",
+    "# # 获取重构的原始数据\n",
+    "high_re= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\high_re.csv\"#数据所在路径\n",
+    "# #我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "high_re = pd.DataFrame(pd.read_csv(high_re))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        column_name\n",
+      "0         -1.426824\n",
+      "1         -1.426819\n",
+      "2         -1.426815\n",
+      "3         -1.426812\n",
+      "4         -1.426810\n",
+      "...             ...\n",
+      "104251    -1.629381\n",
+      "104252    -1.629328\n",
+      "104253    -1.629271\n",
+      "104254    -1.629213\n",
+      "104255    -1.629152\n",
+      "\n",
+      "[104256 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "reconstructed_data_high= high_re\n",
+    "# # 打印重构的原始数据\n",
+    "print(reconstructed_data_high)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# # 假设你已经有了原始数据和重构数据\n",
+    "# # 原始数据\n",
+    "original_data = data1['Power'].values\n",
+    "\n",
+    "# # 创建时间序列（假设时间序列与数据对应）\n",
+    "time = range(len(original_data))\n",
+    "\n",
+    "# # 创建画布和子图\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# # 绘制原始数据\n",
+    "# plt.plot(time, original_data, label='Original Data', color='blue')\n",
+    "\n",
+    "# # 绘制重构数据\n",
+    "plt.plot(reconstructed_data_high[200:1000], label='Reconstructed Data', color='red')\n",
+    "\n",
+    "# # 添加标题和标签\n",
+    "plt.title('Comparison between Original and reconstructed_data_high')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Power')\n",
+    "plt.legend()\n",
+    "\n",
+    "# # 显示图形\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data3=data1.iloc[:,:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Temp   Humidity       GHI       DHI  Rainfall  column_name\n",
+      "0       19.779453  40.025826  3.232706  1.690531       0.0    -1.426824\n",
+      "1       19.714937  39.605961  3.194991  1.576346       0.0    -1.426819\n",
+      "2       19.549330  39.608631  3.070866  1.576157       0.0    -1.426815\n",
+      "3       19.405870  39.680702  3.038623  1.482489       0.0    -1.426812\n",
+      "4       19.387363  39.319881  2.656474  1.134153       0.0    -1.426810\n",
+      "...           ...        ...       ...       ...       ...          ...\n",
+      "104251  13.303740  34.212711  1.210789  0.787026       0.0    -1.629381\n",
+      "104252  13.120920  34.394939  2.142980  1.582670       0.0    -1.629328\n",
+      "104253  12.879215  35.167400  1.926214  1.545889       0.0    -1.629271\n",
+      "104254  12.915867  35.359989  1.317695  0.851529       0.0    -1.629213\n",
+      "104255  13.134816  34.500034  1.043269  0.597816       0.0    -1.629152\n",
+      "\n",
+      "[104256 rows x 6 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# # 创建data3和imf1_array对应的DataFrame\n",
+    "data3_df = pd.DataFrame(data3)\n",
+    "imf1_df = pd.DataFrame(reconstructed_data_high)\n",
+    "\n",
+    "# # 合并data3_df和imf1_df\n",
+    "merged_df = pd.concat([data3_df, imf1_df], axis=1)\n",
+    "\n",
+    "merged_df = merged_df.iloc[:104256]\n",
+    "\n",
+    "# # 打印合并后的表\n",
+    "print(merged_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104256, 6)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(104256, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 使用MinMaxScaler进行归一化\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "scaledData1 = scaler.fit_transform(merged_df)\n",
+    "print(scaledData1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            0         1         2         3    4         5   0(t-96)  \\\n",
+      "96   0.555631  0.349673  0.190042  0.040558  0.0  0.245160  0.490360   \n",
+      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.264683  0.489088   \n",
+      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.283988  0.485824   \n",
+      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.303131  0.482997   \n",
+      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.322308  0.482632   \n",
+      "\n",
+      "      1(t-96)   2(t-96)   3(t-96)  ...    2(t-1)    3(t-1)  4(t-1)    5(t-1)  \\\n",
+      "96   0.369105  0.002088  0.002013  ...  0.166009  0.036794     0.0  0.225396   \n",
+      "97   0.364859  0.002061  0.001839  ...  0.190042  0.040558     0.0  0.245160   \n",
+      "98   0.364886  0.001973  0.001839  ...  0.211335  0.044613     0.0  0.264683   \n",
+      "99   0.365615  0.001950  0.001697  ...  0.229657  0.047549     0.0  0.283988   \n",
+      "100  0.361965  0.001679  0.001167  ...  0.247775  0.053347     0.0  0.303131   \n",
+      "\n",
+      "       0(t+1)    1(t+1)    2(t+1)    3(t+1)  4(t+1)    5(t+1)  \n",
+      "96   0.564819  0.315350  0.211335  0.044613     0.0  0.264683  \n",
+      "97   0.576854  0.288321  0.229657  0.047549     0.0  0.283988  \n",
+      "98   0.581973  0.268243  0.247775  0.053347     0.0  0.303131  \n",
+      "99   0.586026  0.264586  0.266058  0.057351     0.0  0.322308  \n",
+      "100  0.590772  0.258790  0.282900  0.060958     0.0  0.340588  \n",
+      "\n",
+      "[5 rows x 588 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_steps_in =96 #历史时间长度\n",
+    "n_steps_out=1#预测时间长度\n",
+    "processedData1 = time_series_to_supervised(scaledData1,n_steps_in,n_steps_out)\n",
+    "print(processedData1.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# processedData1.to_csv('processedData1.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']\n",
+    "data_y = processedData1.loc[:,'5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104159, 576)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96        0.245160\n",
+       "97        0.264683\n",
+       "98        0.283988\n",
+       "99        0.303131\n",
+       "100       0.322308\n",
+       "            ...   \n",
+       "104250    0.000090\n",
+       "104251    0.000099\n",
+       "104252    0.000109\n",
+       "104253    0.000118\n",
+       "104254    0.000128\n",
+       "Name: 5, Length: 104159, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104159,)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(83328, 96, 6) (83328,) (20831, 96, 6) (20831,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 7.划分训练集和测试集\n",
+    "\n",
+    "test_size = int(len(data_x) * 0.2)\n",
+    "# 计算训练集和测试集的索引范围\n",
+    "train_indices = range(len(data_x) - test_size)\n",
+    "test_indices = range(len(data_x) - test_size, len(data_x))\n",
+    "\n",
+    "# 根据索引范围划分数据集\n",
+    "train_X1 = data_x.iloc[train_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
+    "test_X1 = data_x.iloc[test_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
+    "train_y = data_y.iloc[train_indices].values\n",
+    "test_y = data_y.iloc[test_indices].values\n",
+    "\n",
+    "\n",
+    "# # 多次运行代码时希望得到相同的数据分割，可以设置 random_state 参数为一个固定的整数值\n",
+    "# train_X1,test_X1, train_y, test_y = train_test_split(data_x.values, data_y.values, test_size=0.2, random_state=343)\n",
+    "# reshape input to be 3D [samples, timesteps, features]\n",
+    "train_X = train_X1.reshape((train_X1.shape[0], n_steps_in, scaledData1.shape[1]))\n",
+    "test_X = test_X1.reshape((test_X1.shape[0], n_steps_in,scaledData1.shape[1]))\n",
+    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)\n",
+    "# 使用train_test_split函数划分训练集和测试集，测试集的比重是40%。\n",
+    "# 然后将train_X1、test_X1进行一个升维，变成三维，维数分别是[samples,timesteps,features]。\n",
+    "# 打印一下他们的shape：\\\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(83328, 96, 6)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_X1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From d:\\Anaconda3\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py:192: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">832</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ max_pooling1d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)      │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ bidirectional       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">95</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,920</span> │ max_pooling1d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ attention_with_imp… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>,     │     <span style=\"color: #00af00; text-decoration-color: #00af00\">66,304</span> │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AttentionWithImpr…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>,   │            │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
+       "│                     │ <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]      │            │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ attention_with_i… │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │ global_average_p… │\n",
+       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m6\u001b[0m)     │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
+       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │        \u001b[38;5;34m832\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ max_pooling1d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ conv1d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
+       "│ (\u001b[38;5;33mMaxPooling1D\u001b[0m)      │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ bidirectional       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m95\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m49,920\u001b[0m │ max_pooling1d[\u001b[38;5;34m0\u001b[0m]… │\n",
+       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ attention_with_imp… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m,     │     \u001b[38;5;34m66,304\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
+       "│ (\u001b[38;5;33mAttentionWithImpr…\u001b[0m │ \u001b[38;5;34m128\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m,   │            │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
+       "│                     │ \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)]      │            │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ attention_with_i… │\n",
+       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │        \u001b[38;5;34m129\u001b[0m │ global_average_p… │\n",
+       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">117,185</span> (457.75 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m117,185\u001b[0m (457.75 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">117,185</span> (457.75 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m117,185\u001b[0m (457.75 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import Input, Conv1D, Bidirectional, GlobalAveragePooling1D, Dense, GRU, MaxPooling1D\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.initializers import RandomUniform\n",
+    "class AttentionWithImproveRelativePositionEncoding(tf.keras.layers.Layer):\n",
+    "    def __init__(self, d_model, num_heads, max_len=5000):\n",
+    "        super(AttentionWithImproveRelativePositionEncoding, self).__init__()\n",
+    "        self.num_heads = num_heads\n",
+    "        self.d_model = d_model\n",
+    "        self.max_len = max_len\n",
+    "        self.wq = tf.keras.layers.Dense(d_model)\n",
+    "        self.wk = tf.keras.layers.Dense(d_model)\n",
+    "        self.wv = tf.keras.layers.Dense(d_model)\n",
+    "        self.dense = tf.keras.layers.Dense(d_model)\n",
+    "        self.position_encoding = ImproveRelativePositionEncoding(d_model)\n",
+    "\n",
+    "    def call(self, v, k, q, mask):\n",
+    "        batch_size = tf.shape(q)[0]\n",
+    "        q = self.wq(q)\n",
+    "        k = self.wk(k)\n",
+    "        v = self.wv(v)\n",
+    "\n",
+    "        # 添加位置编码\n",
+    "        k += self.position_encoding (k)\n",
+    "        q += self.position_encoding (q)\n",
+    "\n",
+    "        q = self.split_heads(q, batch_size)\n",
+    "        k = self.split_heads(k, batch_size)\n",
+    "        v = self.split_heads(v, batch_size)\n",
+    "\n",
+    "        scaled_attention, attention_weights = self.scaled_dot_product_attention(q, k, v, mask)\n",
+    "        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])\n",
+    "        concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))\n",
+    "        output = self.dense(concat_attention)\n",
+    "        return output, attention_weights\n",
+    "\n",
+    "    def split_heads(self, x, batch_size):\n",
+    "        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.d_model // self.num_heads))\n",
+    "        return tf.transpose(x, perm=[0, 2, 1, 3])\n",
+    "\n",
+    "    def scaled_dot_product_attention(self, q, k, v, mask):\n",
+    "        matmul_qk = tf.matmul(q, k, transpose_b=True)\n",
+    "        dk = tf.cast(tf.shape(k)[-1], tf.float32)\n",
+    "        scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n",
+    "\n",
+    "        if mask is not None:\n",
+    "            scaled_attention_logits += (mask * -1e9)\n",
+    "\n",
+    "        attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)\n",
+    "        output = tf.matmul(attention_weights, v)\n",
+    "        return output, attention_weights\n",
+    "\n",
+    "class ImproveRelativePositionEncoding(tf.keras.layers.Layer):\n",
+    "    def __init__(self, d_model, max_len=5000):\n",
+    "        super(ImproveRelativePositionEncoding, self).__init__()\n",
+    "        self.max_len = max_len\n",
+    "        self.d_model = d_model\n",
+    "        # 引入可变化的参数u和v进行线性变化\n",
+    "        self.u = self.add_weight(shape=(self.d_model,),\n",
+    "                                 initializer=RandomUniform(),\n",
+    "                                 trainable=True)\n",
+    "        self.v = self.add_weight(shape=(self.d_model,),\n",
+    "                                 initializer=RandomUniform(),\n",
+    "                                 trainable=True)\n",
+    "    def call(self, inputs):\n",
+    "        seq_length = inputs.shape[1]\n",
+    "        pos_encoding = self.relative_positional_encoding(seq_length, self.d_model)\n",
+    "        \n",
+    "        # 调整原始的相对位置编码公式，将u和v参数融入其中\n",
+    "        pe_with_params = pos_encoding * self.u+ pos_encoding * self.v\n",
+    "        return inputs + pe_with_params\n",
+    "\n",
+    "    def relative_positional_encoding(self, position, d_model):\n",
+    "        pos = tf.range(position, dtype=tf.float32)\n",
+    "        i = tf.range(d_model, dtype=tf.float32)\n",
+    "        \n",
+    "        angles = 1 / tf.pow(10000.0, (2 * (i // 2)) / tf.cast(d_model, tf.float32))\n",
+    "        angle_rads = tf.einsum('i,j->ij', pos, angles)\n",
+    "        #保留了sinous机制\n",
+    "        # Apply sin to even indices; 2i\n",
+    "        angle_rads_sin = tf.sin(angle_rads[:, 0::2])\n",
+    "        # Apply cos to odd indices; 2i+1\n",
+    "        angle_rads_cos = tf.cos(angle_rads[:, 1::2])\n",
+    "\n",
+    "        pos_encoding = tf.stack([angle_rads_sin, angle_rads_cos], axis=2)\n",
+    "        pos_encoding = tf.reshape(pos_encoding, [1, position, d_model])\n",
+    "\n",
+    "        return pos_encoding\n",
+    "\n",
+    "\n",
+    "\n",
+    "def PosConv1biGRUWithSelfAttention(input_shape, gru_units, num_heads):\n",
+    "    inputs = Input(shape=input_shape)\n",
+    "    # CNN layer\n",
+    "    cnn_layer = Conv1D(filters=64, kernel_size=2, activation='relu')(inputs)\n",
+    "    cnn_layer = MaxPooling1D(pool_size=1)(cnn_layer)\n",
+    "    gru_output = Bidirectional(GRU(gru_units, return_sequences=True))(cnn_layer)\n",
+    "    \n",
+    "    # Apply Self-Attention\n",
+    "    self_attention =AttentionWithImproveRelativePositionEncoding(d_model=gru_units*2, num_heads=num_heads)\n",
+    "    gru_output, _ = self_attention(gru_output, gru_output, gru_output, mask=None)\n",
+    "    \n",
+    "    pool1 = GlobalAveragePooling1D()(gru_output)\n",
+    "    output = Dense(1)(pool1)\n",
+    "    \n",
+    "    return Model(inputs=inputs, outputs=output)\n",
+    "\n",
+    "\n",
+    "input_shape = (96, 6)\n",
+    "gru_units = 64\n",
+    "num_heads = 8\n",
+    "\n",
+    "# Create model\n",
+    "model = PosConv1biGRUWithSelfAttention(input_shape, gru_units, num_heads)\n",
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 65ms/step - loss: 0.0178 - val_loss: 0.0018\n",
+      "Epoch 2/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 64ms/step - loss: 0.0011 - val_loss: 0.0016\n",
+      "Epoch 3/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 71ms/step - loss: 0.0010 - val_loss: 0.0024\n",
+      "Epoch 4/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 64ms/step - loss: 9.7998e-04 - val_loss: 0.0015\n",
+      "Epoch 5/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 59ms/step - loss: 0.0010 - val_loss: 0.0015\n",
+      "Epoch 6/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 54ms/step - loss: 0.0010 - val_loss: 0.0016\n",
+      "Epoch 7/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 58ms/step - loss: 9.6638e-04 - val_loss: 0.0015\n",
+      "Epoch 8/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 58ms/step - loss: 8.8641e-04 - val_loss: 0.0017\n",
+      "Epoch 9/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 50ms/step - loss: 9.5932e-04 - val_loss: 0.0015\n",
+      "Epoch 10/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 51ms/step - loss: 9.3643e-04 - val_loss: 0.0015\n",
+      "Epoch 11/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 52ms/step - loss: 9.2035e-04 - val_loss: 0.0017\n",
+      "Epoch 12/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 52ms/step - loss: 8.8128e-04 - val_loss: 0.0017\n",
+      "Epoch 13/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 57ms/step - loss: 8.7290e-04 - val_loss: 0.0016\n",
+      "Epoch 14/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 59ms/step - loss: 8.5652e-04 - val_loss: 0.0016\n",
+      "Epoch 15/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 62ms/step - loss: 8.6573e-04 - val_loss: 0.0018\n",
+      "Epoch 16/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 61ms/step - loss: 9.3113e-04 - val_loss: 0.0015\n",
+      "Epoch 17/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 61ms/step - loss: 8.6217e-04 - val_loss: 0.0015\n",
+      "\u001b[1m651/651\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 17ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compile and train the model\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "\n",
+    "# 定义早停机制\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min')\n",
+    "\n",
+    "# 拟合模型，并添加早停机制和模型检查点\n",
+    "history = model.fit(train_X, train_y, epochs=100, batch_size=64, validation_data=(test_X, test_y), \n",
+    "                    callbacks=[early_stopping])\n",
+    "# 预测\n",
+    "lstm_pred = model.predict(test_X)\n",
+    "# 将预测结果的形状修改为与原始数据相同的形状"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(history.history['loss'], label='train')\n",
+    "plt.plot(history.history['val_loss'], label='test')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20831, 1)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lstm_pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20831,)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y1=test_y.reshape(20831,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4.52189913e-01],\n",
+       "       [3.12516873e-01],\n",
+       "       [3.25310588e-01],\n",
+       "       ...,\n",
+       "       [1.08522631e-04],\n",
+       "       [1.18219088e-04],\n",
+       "       [1.28327022e-04]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_y1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1 = np.broadcast_to(lstm_pred, (20831, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y2 = np.broadcast_to(test_y1, (20831, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 反归一化\n",
+    "inv_forecast_y = scaler.inverse_transform(results1)\n",
+    "inv_test_y = scaler.inverse_transform(test_y2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.78428369e+01,  4.82409691e+01,  6.37156385e+02,\n",
+       "         2.97801603e+02,  1.07621239e+01,  9.90052500e-01],\n",
+       "       [ 1.07562527e+01,  3.44305945e+01,  4.40440713e+02,\n",
+       "         2.05929459e+02,  7.43790432e+00,  1.80780551e-01],\n",
+       "       [ 1.14053667e+01,  3.56955916e+01,  4.58459395e+02,\n",
+       "         2.14344726e+02,  7.74239484e+00,  2.54907916e-01],\n",
+       "       ...,\n",
+       "       [-5.09439462e+00,  3.54076535e+00,  4.44428011e-01,\n",
+       "         4.37940726e-01,  2.58283957e-03, -1.62932764e+00],\n",
+       "       [-5.09390265e+00,  3.54172410e+00,  4.58084512e-01,\n",
+       "         4.44318723e-01,  2.81361533e-03, -1.62927146e+00],\n",
+       "       [-5.09338980e+00,  3.54272354e+00,  4.72320538e-01,\n",
+       "         4.50967376e-01,  3.05418424e-03, -1.62921289e+00]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inv_test_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test RMSE: 0.223\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 计算均方根误差\n",
+    "rmse = sqrt(mean_squared_error(inv_test_y[:,5], inv_forecast_y[:,5]))\n",
+    "print('Test RMSE: %.3f' % rmse)\n",
+    "#画图\n",
+    "plt.figure(figsize=(16,8))\n",
+    "plt.plot(inv_test_y[300:3000,5], label='true')\n",
+    "plt.plot(inv_forecast_y[300:3000,5], label='pre')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_squared_error: 0.0014791752952266549\n",
+      "mean_absolute_error: 0.013799955472387545\n",
+      "rmse: 0.0384600480398381\n",
+      "r2 score: 0.9904178817149276\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
+    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score\n",
+    "print('mean_squared_error:', mean_squared_error(lstm_pred, test_y))  # mse)\n",
+    "print(\"mean_absolute_error:\", mean_absolute_error(lstm_pred, test_y))  # mae\n",
+    "print(\"rmse:\", sqrt(mean_squared_error(lstm_pred,test_y)))\n",
+    "print(\"r2 score:\", r2_score(inv_test_y[5000:10000], inv_forecast_y[5000:10000]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df1.to_csv('高频re_test.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame(inv_forecast_y[:,5], columns=['column_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df2.to_csv('高频re_forecast.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/iceemdan信号重构.ipynb b/iceemdan信号重构.ipynb
new file mode 100644
index 0000000..ce34f6b
--- /dev/null
+++ b/iceemdan信号重构.ipynb
@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
+      "C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
+      "  from pandas.core import (\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import sqrt\n",
+    "from numpy import concatenate\n",
+    "from matplotlib import pyplot\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from tensorflow.keras import Sequential\n",
+    "\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.layers import LSTM\n",
+    "from tensorflow.keras.layers import Dropout\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加载数据\n",
+    "path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\9996低频_forecast.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "f_low= pd.DataFrame(pd.read_csv(path1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加载数据\n",
+    "path2 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\99939高频re_forecast.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "f_high= pd.DataFrame(pd.read_csv(path2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path3= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\9996低频_test.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "true_low= pd.DataFrame(pd.read_csv(path3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path4= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\99939高频re_test.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "true_high= pd.DataFrame(pd.read_csv(path4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path5= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\test.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "true_2= pd.DataFrame(pd.read_csv(path5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    <tr>\n",
+       "      <th>1561</th>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1562 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      column_name\n",
+       "0        4.607052\n",
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+       "1557     0.022011\n",
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+       "1560     0.022857\n",
+       "1561     0.018875\n",
+       "\n",
+       "[1562 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pre_data=f_low+f_high\n",
+    "pre_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th></th>\n",
+       "      <th>column_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.679599e+00</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.675801e+00</td>\n",
+       "    </tr>\n",
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+       "      <td>4.636000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.572200e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.525266e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1562 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       column_name\n",
+       "0     4.679599e+00\n",
+       "1     4.675801e+00\n",
+       "2     4.636000e+00\n",
+       "3     4.572200e+00\n",
+       "4     4.525266e+00\n",
+       "...            ...\n",
+       "1557  6.661338e-16\n",
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+       "1561  4.440892e-16\n",
+       "\n",
+       "[1562 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true=true_low+true_high\n",
+    "true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16,8))\n",
+    "plt.plot(true, label='true')\n",
+    "plt.plot(pre_data, label='pre')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16,8))\n",
+    "plt.plot(true_2, label='true')\n",
+    "plt.plot(pre_data, label='pre')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "找论文  引用  峰值达不到 偏低 情况 通过分解之后 提高了 加在分析 结论\n",
+    "两个数据集篇幅一样  附录 图 未来数据引入 文章对比   3个亮点"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_squared_error: 0.0020508589297795984\n",
+      "mean_absolute_error: 0.024749394165108834\n",
+      "rmse: 0.04528640998996938\n",
+      "r2 score: 0.9993959872891868\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
+    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score\n",
+    "print('mean_squared_error:', mean_squared_error(pre_data, true))  # mse)\n",
+    "print(\"mean_absolute_error:\", mean_absolute_error(pre_data, true))  # mae\n",
+    "print(\"rmse:\", sqrt(mean_squared_error(pre_data, true)))\n",
+    "print(\"r2 score:\", r2_score(pre_data[:], true[:]))#"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1562, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 使用MinMaxScaler进行归一化\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "pre = scaler.fit_transform(pre_data)\n",
+    "print(pre.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1562, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "true_data = scaler.fit_transform(true)\n",
+    "print(true_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_squared_error: 0.00010504255695631394\n",
+      "mean_absolute_error: 0.00643843807544528\n",
+      "rmse: 0.010249027122430398\n",
+      "r2 score: 0.9993959872891868\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
+    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score\n",
+    "print('mean_squared_error:', mean_squared_error(pre, true_data))  # mse)\n",
+    "print(\"mean_absolute_error:\", mean_absolute_error(pre, true_data))  # mae\n",
+    "print(\"rmse:\", sqrt(mean_squared_error(pre, true_data)))\n",
+    "print(\"r2 score:\", r2_score(pre_data, true_2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/iceemdan分解 逐步分解.ipynb b/iceemdan分解 逐步分解.ipynb
new file mode 100644
index 0000000..223087f
--- /dev/null
+++ b/iceemdan分解 逐步分解.ipynb	
@@ -0,0 +1,418 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt\n",
+    "from numpy import concatenate\n",
+    "from matplotlib import pyplot\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from tensorflow.keras import Sequential\n",
+    "\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.layers import LSTM\n",
+    "from tensorflow.keras.layers import Dropout\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加载数据\n",
+    "path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\data6.csv\"#数据所在路径\n",
+    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
+    "datas1 = pd.DataFrame(pd.read_csv(path1))\n",
+    "#我只取了data表里的第3、23、16、17、18、19、20、21、27列，如果取全部列的话这一行可以去掉\n",
+    "# data1 = datas1.iloc[:,np.r_[3,23,16:22,27]]\n",
+    "data1=datas1.interpolate()\n",
+    "values1 = data1.values\n",
+    "print(data1.head())\n",
+    "print(data1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PyEMD import EMD\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "def ICEEMDAN(data, num_siftings=18, Nstd=0.2, NR=100):\n",
+    "    \"\"\"\n",
+    "    改进的完全集合经验模态分解与自适应噪声（ICEEMDAN）。\n",
+    "    \"\"\"\n",
+    "    emd = EMD()\n",
+    "    T = len(data)\n",
+    "    std_data = np.std(data)\n",
+    "    \n",
+    "    # 通过添加白噪声获取数据的 IMFs 集合\n",
+    "    E_IMFs = np.zeros((NR, T, num_siftings))\n",
+    "    for r in range(NR):\n",
+    "        wn = np.random.normal(scale=std_data*Nstd, size=T)\n",
+    "        noisy_data = data + wn\n",
+    "        \n",
+    "        # 分解带噪声的数据\n",
+    "        imfs = emd.emd(noisy_data)\n",
+    "        \n",
+    "        # 仅保留前 `num_siftings` 个 IMFs\n",
+    "        for i in range(min(num_siftings, imfs.shape[0])):\n",
+    "            E_IMFs[r, :, i] = imfs[i]\n",
+    "    \n",
+    "    # 所有实验的 IMFs 平均值\n",
+    "    mean_IMFs = np.mean(E_IMFs, axis=0)\n",
+    "    \n",
+    "    return mean_IMFs\n",
+    "\n",
+    "# 假设您有一个名为 'data1' 的 DataFrame，其中包含一个 'Power' 列\n",
+    "data_power = data1['Power'].values\n",
+    "\n",
+    "# 定义分解的数据窗口大小\n",
+    "window_size = 104256\n",
+    "\n",
+    "# 遍历以 'window_size' 为单位的数据块\n",
+    "for i in range(0, len(data_power), window_size):\n",
+    "    # 提取当前数据块\n",
+    "    current_data = data_power[i:i+window_size]\n",
+    "    \n",
+    "    # 对当前数据块应用 ICEEMDAN 分解\n",
+    "    IMFs = ICEEMDAN(current_data)\n",
+    "    \n",
+    "    # 创建一个新的 DataFrame 来保存 ICEEMDAN 分解结果\n",
+    "    data_ICEEMDAN = pd.DataFrame(IMFs, columns=[f'IMF{i}' for i in range(IMFs.shape[1])])\n",
+    "    \n",
+    "    # 计算 IMFs 的总和\n",
+    "    IMFs_sum = data_ICEEMDAN[[f'IMF{i}' for i in range(IMFs.shape[1])]].sum(axis=1)\n",
+    "    \n",
+    "    # 计算 IMFs 总和与原始信号之间的差值\n",
+    "    residue = current_data - IMFs_sum\n",
+    "    \n",
+    "    # 将残差添加到 DataFrame\n",
+    "    data_ICEEMDAN['Residue'] = residue\n",
+    "    \n",
+    "    # 打印当前数据块的分解结果\n",
+    "    print(f\"数据块 {i+1}-{i+len(current_data)} 的分解结果:\")\n",
+    "    print(data_ICEEMDAN)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将分解后的 IMFs 与残差相加以恢复原始数据\n",
+    "reconstructed_signal = IMFs_sum + residue\n",
+    "\n",
+    "# 绘制重构的数据与原始数据进行比较\n",
+    "plt.figure(figsize=(30, 18))\n",
+    "plt.plot(current_data[0:2900], label='Original Signal', color='blue')\n",
+    "plt.plot(reconstructed_signal[0:2900], label='Reconstructed Signal', color='red')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Amplitude')\n",
+    "plt.title('Comparison between Original and Reconstructed Signal')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 设置全局字体大小\n",
+    "plt.rcParams.update({'font.size': 60})  # 设置字体大小为 16\n",
+    "\n",
+    "# 在这里放置您的绘图代码\n",
+    "num_imfs = IMFs.shape[1]\n",
+    "\n",
+    "plt.figure(figsize=(64,128))\n",
+    "\n",
+    "# 绘制每个 IMF\n",
+    "for i in range(num_imfs):\n",
+    "    if i <= 19:  # 只绘制前 14 个 IMF\n",
+    "        plt.subplot(num_imfs, 1, i + 1)\n",
+    "        plt.plot(data_ICEEMDAN[f'IMF{i}'], label=f'IMF {i}')\n",
+    "        plt.legend()\n",
+    "plt.legend()\n",
+    "\n",
+    "# 绘制残差\n",
+    "plt.subplot(num_imfs + 1, 1, num_imfs + 1)\n",
+    "plt.plot(data_ICEEMDAN[f'IMF{17}'], label='Residue', color='red')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.signal import welch\n",
+    "\n",
+    "def plot_frequency_spectrum(signal, sampling_rate, title):\n",
+    "    \"\"\"\n",
+    "    绘制信号的频谱图。\n",
+    "    \n",
+    "    参数：\n",
+    "    - signal: 输入信号\n",
+    "    - sampling_rate: 信号的采样率\n",
+    "    - title: 图表标题\n",
+    "    \"\"\"\n",
+    "    f, Pxx = welch(signal, fs=sampling_rate, nperseg=len(signal))\n",
+    "    plt.figure(figsize=(10, 5))\n",
+    "    plt.semilogy(f, Pxx)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Frequency (Hz)')\n",
+    "    plt.ylabel('Power spectral density')\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n",
+    "\n",
+    "def compute_zero_crossings(signal):\n",
+    "    \"\"\"\n",
+    "    计算信号的过零率。\n",
+    "    \"\"\"\n",
+    "    zero_crossings = np.where(np.diff(np.sign(signal)))[0]\n",
+    "    zero_crossing_rate = len(zero_crossings) / len(signal)\n",
+    "    return zero_crossing_rate\n",
+    "\n",
+    "def classify_frequency_component(imfs, threshold):\n",
+    "    \"\"\"\n",
+    "    将每个 IMF 分为高频和低频成分。\n",
+    "    \"\"\"\n",
+    "    high_frequency_imfs = []\n",
+    "    low_frequency_imfs = []\n",
+    "    for i in range(imfs.shape[1]):\n",
+    "        imf = imfs[:, i]\n",
+    "        zero_crossing_rate = compute_zero_crossings(imf)\n",
+    "        if zero_crossing_rate > threshold:\n",
+    "            high_frequency_imfs.append(imf)\n",
+    "        else:\n",
+    "            low_frequency_imfs.append(imf)\n",
+    "    return high_frequency_imfs, low_frequency_imfs\n",
+    "\n",
+    "# 定义过零率的阈值\n",
+    "threshold = 0.2  # 可根据具体情况调整阈值\n",
+    "\n",
+    "# 根据过零率判断高频和低频成分\n",
+    "high_freq_imfs, low_freq_imfs = classify_frequency_component(IMFs, threshold)\n",
+    "sampling_rate = 1000 \n",
+    "# 可选：绘制高频和低频成分的频谱图\n",
+    "for i, imf in enumerate(high_freq_imfs):\n",
+    " plot_frequency_spectrum(imf, sampling_rate, f'High Frequency IMF {i+1}')\n",
+    "\n",
+    "for i, imf in enumerate(low_freq_imfs):\n",
+    " plot_frequency_spectrum(imf, sampling_rate, f'Low Frequency IMF {i+1}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 打印高频和低频成分的 IMF 列表\n",
+    "print(\"High Frequency IMFs:\")\n",
+    "for i, imf in enumerate(high_freq_imfs):\n",
+    "    print(f\"IMF {i+1}: {imf}\")\n",
+    "\n",
+    "print(\"\\nLow Frequency IMFs:\")\n",
+    "for i, imf in enumerate(low_freq_imfs):\n",
+    "    print(f\"IMF {i+1}: {imf}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# 将高频和低频信号相加\n",
+    "high_freq_sum = np.sum(high_freq_imfs, axis=0)\n",
+    "low_freq_sum = np.sum(low_freq_imfs, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_freq_sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "low_freq_sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "re_low=residue+low_freq_sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "re_high=residue+high_freq_sum\n",
+    "re_high"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df_low = pd.DataFrame(low_freq_sum, columns=['column_name'])\n",
+    "df_low.to_csv('iceemdan_reconstructed_data_low.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df_high = pd.DataFrame(high_freq_sum, columns=['column_name'])\n",
+    "df_high.to_csv('iceemdan_reconstructed_data_high.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df_high = pd.DataFrame(residue, columns=['column_name'])\n",
+    "df_high.to_csv('iceemdan_reconstructed_data_residue.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df_re_low = pd.DataFrame(re_low, columns=['column_name'])\n",
+    "df_re_low.to_csv('iceemdan_reconstructed_data_re_low.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df_re_high = pd.DataFrame(re_high, columns=['column_name'])\n",
+    "df_re_high.to_csv('iceemdan_reconstructed_data_re_high.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMFs_sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df_high = pd.DataFrame(IMFs_sum, columns=['column_name'])\n",
+    "df_high.to_csv('iceemdan_reconstructed_data_IMFs_sum.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}