From d13bd0306d969444a4f8cea07a337e0745f03dee Mon Sep 17 00:00:00 2001
From: hanyp <hanyp@qibebt.ac.cn>
Date: Thu, 21 Nov 2024 13:54:50 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
 =?UTF-8?q?=20''?=
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---
 iceemdan-low-LSTM.ipynb                       | 1035 ++++++++++++++++
 ...-筛选-high-ConvBiGruAttention copy 2.ipynb | 1099 +++++++++++++++++
 iceemdan信号重构.ipynb                        |  740 +++++++++--
 3 files changed, 2768 insertions(+), 106 deletions(-)
 create mode 100644 iceemdan-low-LSTM.ipynb
 create mode 100644 iceemdan-筛选-high-ConvBiGruAttention copy 2.ipynb

diff --git a/iceemdan-low-LSTM.ipynb b/iceemdan-low-LSTM.ipynb
new file mode 100644
index 0000000..1764c64
--- /dev/null
+++ b/iceemdan-low-LSTM.ipynb
@@ -0,0 +1,1035 @@
+{
+ "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",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
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+       "      <th></th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>1.460307</td>\n",
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+       "      <th>2</th>\n",
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+       "      <th>4</th>\n",
+       "      <td>1.461071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <th>104253</th>\n",
+       "      <td>1.665650</td>\n",
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+       "      <th>104254</th>\n",
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+       "      <td>1.667887</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>104256 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        column_name\n",
+       "0          1.460307\n",
+       "1          1.460504\n",
+       "2          1.460698\n",
+       "3          1.460886\n",
+       "4          1.461071\n",
+       "...             ...\n",
+       "104251     1.663370\n",
+       "104252     1.664516\n",
+       "104253     1.665650\n",
+       "104254     1.666774\n",
+       "104255     1.667887\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     1.460307\n",
+      "1       19.714937  39.605961  3.194991  1.576346       0.0     1.460504\n",
+      "2       19.549330  39.608631  3.070866  1.576157       0.0     1.460698\n",
+      "3       19.405870  39.680702  3.038623  1.482489       0.0     1.460886\n",
+      "4       19.387363  39.319881  2.656474  1.134153       0.0     1.461071\n",
+      "...           ...        ...       ...       ...       ...          ...\n",
+      "104251  13.303740  34.212711  1.210789  0.787026       0.0     1.663370\n",
+      "104252  13.120920  34.394939  2.142980  1.582670       0.0     1.664516\n",
+      "104253  12.879215  35.167400  1.926214  1.545889       0.0     1.665650\n",
+      "104254  12.915867  35.359989  1.317695  0.851529       0.0     1.666774\n",
+      "104255  13.134816  34.500034  1.043269  0.597816       0.0     1.667887\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  0.777807  0.490360   \n",
+      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.777601  0.489088   \n",
+      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.777391  0.485824   \n",
+      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.777176  0.482997   \n",
+      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.776958  0.482632   \n",
+      "\n",
+      "      1(t-96)   2(t-96)   3(t-96)  ...    2(t+2)    3(t+2)  4(t+2)    5(t+2)  \\\n",
+      "96   0.369105  0.002088  0.002013  ...  0.229657  0.047549     0.0  0.777391   \n",
+      "97   0.364859  0.002061  0.001839  ...  0.247775  0.053347     0.0  0.777176   \n",
+      "98   0.364886  0.001973  0.001839  ...  0.266058  0.057351     0.0  0.776958   \n",
+      "99   0.365615  0.001950  0.001697  ...  0.282900  0.060958     0.0  0.776735   \n",
+      "100  0.361965  0.001679  0.001167  ...  0.299668  0.065238     0.0  0.776508   \n",
+      "\n",
+      "       0(t+3)    1(t+3)    2(t+3)    3(t+3)  4(t+3)    5(t+3)  \n",
+      "96   0.581973  0.268243  0.247775  0.053347     0.0  0.777176  \n",
+      "97   0.586026  0.264586  0.266058  0.057351     0.0  0.776958  \n",
+      "98   0.590772  0.258790  0.282900  0.060958     0.0  0.776735  \n",
+      "99   0.600396  0.249246  0.299668  0.065238     0.0  0.776508  \n",
+      "100  0.607019  0.247850  0.313694  0.066189     0.0  0.776277  \n",
+      "\n",
+      "[5 rows x 600 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_steps_in =96 #历史时间长度\n",
+    "n_steps_out=3#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(t+3)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104157, 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        0.777176\n",
+       "97        0.776958\n",
+       "98        0.776735\n",
+       "99        0.776508\n",
+       "100       0.776277\n",
+       "            ...   \n",
+       "104248    0.897435\n",
+       "104249    0.898092\n",
+       "104250    0.898742\n",
+       "104251    0.899387\n",
+       "104252    0.900025\n",
+       "Name: 5(t+3), Length: 104157, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104157,)"
+      ]
+     },
+     "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": [
+      "(83325, 96, 6) (83325,) (10417, 96, 6) (10417,) (10415, 96, 6) (10415,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 计算训练集、验证集和测试集的大小\n",
+    "train_size = int(len(data_x) * 0.8)\n",
+    "test_size = int(len(data_x) * 0.1)\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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(83325, 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[1m45s\u001b[0m 34ms/step - loss: 0.0071 - val_loss: 1.3979e-05\n",
+      "Epoch 2/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 35ms/step - loss: 1.7388e-05 - val_loss: 2.4750e-05\n",
+      "Epoch 3/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 42ms/step - loss: 9.4934e-06 - val_loss: 2.6778e-06\n",
+      "Epoch 4/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 59ms/step - loss: 7.7084e-06 - val_loss: 8.5239e-06\n",
+      "Epoch 5/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 46ms/step - loss: 1.0285e-05 - val_loss: 7.4017e-06\n",
+      "Epoch 6/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 49ms/step - loss: 4.5950e-06 - val_loss: 4.3379e-06\n",
+      "Epoch 7/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 56ms/step - loss: 7.2545e-06 - val_loss: 5.1982e-05\n",
+      "Epoch 8/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 60ms/step - loss: 8.1455e-06 - val_loss: 5.4236e-06\n",
+      "Epoch 9/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 57ms/step - loss: 4.0686e-06 - val_loss: 1.6651e-06\n",
+      "Epoch 10/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 53ms/step - loss: 4.4366e-06 - val_loss: 1.1472e-06\n",
+      "Epoch 11/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 57ms/step - loss: 5.2050e-06 - val_loss: 1.9424e-07\n",
+      "Epoch 12/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 58ms/step - loss: 2.9417e-06 - val_loss: 7.2545e-06\n",
+      "Epoch 13/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 35ms/step - loss: 3.5579e-06 - val_loss: 8.3836e-07\n",
+      "Epoch 14/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 47ms/step - loss: 2.9325e-06 - val_loss: 1.8872e-06\n",
+      "Epoch 15/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 50ms/step - loss: 1.1996e-06 - val_loss: 4.9818e-07\n",
+      "Epoch 16/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 31ms/step - loss: 1.9083e-06 - val_loss: 1.1571e-06\n",
+      "Epoch 17/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 32ms/step - loss: 2.5659e-06 - val_loss: 2.3767e-07\n",
+      "Epoch 18/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 32ms/step - loss: 1.9273e-06 - val_loss: 2.9061e-07\n",
+      "Epoch 19/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 32ms/step - loss: 1.8791e-06 - val_loss: 2.7131e-06\n",
+      "Epoch 20/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 32ms/step - loss: 2.5186e-06 - val_loss: 1.0457e-06\n",
+      "Epoch 21/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 32ms/step - loss: 1.6832e-06 - val_loss: 3.1923e-06\n",
+      "\u001b[1m326/326\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/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": [
+       "(10415, 1)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lstm_pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10415,)"
+      ]
+     },
+     "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(10415,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.7652725 ],\n",
+       "       [0.76545048],\n",
+       "       [0.76562896],\n",
+       "       ...,\n",
+       "       [0.8987423 ],\n",
+       "       [0.89938682],\n",
+       "       [0.90002507]])"
+      ]
+     },
+     "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, (10415, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y2 = np.broadcast_to(test_y1, (10415, 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([[  33.72769272,   79.19746393, 1078.1022603 ,  503.73660832,\n",
+       "          18.21349214,    1.43294754],\n",
+       "       [  33.73672318,   79.21506254, 1078.35293583,  503.85368135,\n",
+       "          18.2177282 ,    1.43325785],\n",
+       "       [  33.74577882,   79.23271021, 1078.60431013,  503.97108072,\n",
+       "          18.22197608,    1.43356904],\n",
+       "       ...,\n",
+       "       [  40.49954372,   92.3944846 , 1266.08128876,  591.5284767 ,\n",
+       "          21.39007466,    1.66565038],\n",
+       "       [  40.53224485,   92.45821275, 1266.98903575,  591.95242188,\n",
+       "          21.40541432,    1.6667741 ],\n",
+       "       [  40.56462766,   92.52132055, 1267.88794639,  592.37224023,\n",
+       "          21.42060465,    1.66788688]])"
+      ]
+     },
+     "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: 0.003\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: 3.192312293602162e-06\n",
+      "mean_absolute_error: 0.0016849238078766036\n",
+      "rmse: 0.001786704310623938\n",
+      "r2 score: 0.9997179606290253\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('xin9996低频_test(T+3).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('xin9996低频_forecast(T+3).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..53df0b9
--- /dev/null
+++ b/iceemdan-筛选-high-ConvBiGruAttention copy 2.ipynb	
@@ -0,0 +1,1099 @@
+{
+ "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\\完整的模型代码流程 copy\\data66.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": [
+    "# # 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 copy\\t+3\\iceemdan_reconstructed_data_re_high.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": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        column_name\n",
+      "0         -1.460307\n",
+      "1         -1.460504\n",
+      "2         -1.460698\n",
+      "3         -1.460886\n",
+      "4         -1.461071\n",
+      "...             ...\n",
+      "104251    -1.663370\n",
+      "104252    -1.664516\n",
+      "104253    -1.665650\n",
+      "104254    -1.666774\n",
+      "104255    -1.667887\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": 6,
+   "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[90000:], 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": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data3=data1.iloc[:,:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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    -1.460307\n",
+      "1       19.714937  39.605961  3.194991  1.576346       0.0    -1.460504\n",
+      "2       19.549330  39.608631  3.070866  1.576157       0.0    -1.460698\n",
+      "3       19.405870  39.680702  3.038623  1.482489       0.0    -1.460886\n",
+      "4       19.387363  39.319881  2.656474  1.134153       0.0    -1.461071\n",
+      "...           ...        ...       ...       ...       ...          ...\n",
+      "104251  13.303740  34.212711  1.210789  0.787026       0.0    -1.663370\n",
+      "104252  13.120920  34.394939  2.142980  1.582670       0.0    -1.664516\n",
+      "104253  12.879215  35.167400  1.926214  1.545889       0.0    -1.665650\n",
+      "104254  12.915867  35.359989  1.317695  0.851529       0.0    -1.666774\n",
+      "104255  13.134816  34.500034  1.043269  0.597816       0.0    -1.667887\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": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104256, 6)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": 11,
+   "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  0.250386  0.490360   \n",
+      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.268375  0.489088   \n",
+      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.286165  0.485824   \n",
+      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.303808  0.482997   \n",
+      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.321484  0.482632   \n",
+      "\n",
+      "      1(t-96)   2(t-96)   3(t-96)  ...    2(t+2)    3(t+2)  4(t+2)    5(t+2)  \\\n",
+      "96   0.369105  0.002088  0.002013  ...  0.229657  0.047549     0.0  0.286165   \n",
+      "97   0.364859  0.002061  0.001839  ...  0.247775  0.053347     0.0  0.303808   \n",
+      "98   0.364886  0.001973  0.001839  ...  0.266058  0.057351     0.0  0.321484   \n",
+      "99   0.365615  0.001950  0.001697  ...  0.282900  0.060958     0.0  0.338338   \n",
+      "100  0.361965  0.001679  0.001167  ...  0.299668  0.065238     0.0  0.355108   \n",
+      "\n",
+      "       0(t+3)    1(t+3)    2(t+3)    3(t+3)  4(t+3)    5(t+3)  \n",
+      "96   0.581973  0.268243  0.247775  0.053347     0.0  0.303808  \n",
+      "97   0.586026  0.264586  0.266058  0.057351     0.0  0.321484  \n",
+      "98   0.590772  0.258790  0.282900  0.060958     0.0  0.338338  \n",
+      "99   0.600396  0.249246  0.299668  0.065238     0.0  0.355108  \n",
+      "100  0.607019  0.247850  0.313694  0.066189     0.0  0.372185  \n",
+      "\n",
+      "[5 rows x 600 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_steps_in =96 #历史时间长度\n",
+    "n_steps_out=3#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": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']\n",
+    "data_y = processedData1.loc[:,'5(t+3)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104157, 576)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96        0.303808\n",
+       "97        0.321484\n",
+       "98        0.338338\n",
+       "99        0.355108\n",
+       "100       0.372185\n",
+       "            ...   \n",
+       "104248    0.023869\n",
+       "104249    0.023687\n",
+       "104250    0.023507\n",
+       "104251    0.023329\n",
+       "104252    0.023153\n",
+       "Name: 5(t+3), Length: 104157, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(104157,)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(83325, 96, 6) (83325,) (10417, 96, 6) (10417,) (10415, 96, 6) (10415,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 计算训练集、验证集和测试集的大小\n",
+    "train_size = int(len(data_x) * 0.8)\n",
+    "test_size = int(len(data_x) * 0.1)\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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(83325, 96, 6)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_X1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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",
+    "\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=None):\n",
+    "        batch_size = tf.shape(q)[0]\n",
+    "        q = self.wq(q)\n",
+    "        k = self.wk(k)\n",
+    "        v = self.wv(v)\n",
+    "\n",
+    "        # Adding position encoding\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",
+    "        # Introduce learnable parameters u and v\n",
+    "        self.u = self.add_weight(shape=(self.d_model,), initializer=tf.keras.initializers.HeNormal(), trainable=True)\n",
+    "        self.v = self.add_weight(shape=(self.d_model,), initializer=tf.keras.initializers.HeNormal(), trainable=True)\n",
+    "\n",
+    "    def build(self, input_shape):\n",
+    "        super(ImproveRelativePositionEncoding, self).build(input_shape)\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        seq_length = tf.shape(inputs)[1]\n",
+    "        pos_encoding = self.relative_positional_encoding(seq_length, self.d_model)\n",
+    "\n",
+    "        # Adjusting relative position encoding with parameters\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",
+    "\n",
+    "        angle_rads_sin = tf.sin(angle_rads[:, 0::2])\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",
+    "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",
+    "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": 19,
+   "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[1m114s\u001b[0m 86ms/step - loss: 0.0116 - val_loss: 0.0025\n",
+      "Epoch 2/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 84ms/step - loss: 0.0016 - val_loss: 0.0024\n",
+      "Epoch 3/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 96ms/step - loss: 0.0016 - val_loss: 0.0023\n",
+      "Epoch 4/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 87ms/step - loss: 0.0016 - val_loss: 0.0025\n",
+      "Epoch 5/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 79ms/step - loss: 0.0015 - val_loss: 0.0025\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: 0.0015 - val_loss: 0.0025\n",
+      "Epoch 7/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 84ms/step - loss: 0.0014 - val_loss: 0.0027\n",
+      "Epoch 8/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 83ms/step - loss: 0.0015 - val_loss: 0.0024\n",
+      "Epoch 9/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 109ms/step - loss: 0.0014 - val_loss: 0.0023\n",
+      "Epoch 10/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m182s\u001b[0m 140ms/step - loss: 0.0014 - val_loss: 0.0025\n",
+      "Epoch 11/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 110ms/step - loss: 0.0014 - val_loss: 0.0026\n",
+      "Epoch 12/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 89ms/step - loss: 0.0014 - val_loss: 0.0023\n",
+      "Epoch 13/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 89ms/step - loss: 0.0014 - val_loss: 0.0023\n",
+      "Epoch 14/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 104ms/step - loss: 0.0014 - val_loss: 0.0024\n",
+      "Epoch 15/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 86ms/step - loss: 0.0014 - val_loss: 0.0024\n",
+      "Epoch 16/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 81ms/step - loss: 0.0013 - val_loss: 0.0024\n",
+      "Epoch 17/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 84ms/step - loss: 0.0013 - val_loss: 0.0024\n",
+      "Epoch 18/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 96ms/step - loss: 0.0013 - val_loss: 0.0024\n",
+      "Epoch 19/100\n",
+      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m179s\u001b[0m 137ms/step - loss: 0.0013 - val_loss: 0.0025\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",
+    "\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": 20,
+   "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m326/326\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 25ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 预测\n",
+    "lstm_pred = model.predict(test_X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10415, 1)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lstm_pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10415,)"
+      ]
+     },
+     "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(10415,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.06037087],\n",
+       "       [0.06032172],\n",
+       "       [0.06027242],\n",
+       "       ...,\n",
+       "       [0.02350742],\n",
+       "       [0.0233294 ],\n",
+       "       [0.02315312]])"
+      ]
+     },
+     "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, (10415, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_y2 = np.broadcast_to(test_y1, (10415, 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([[-2.03686661,  9.49929284, 85.31799419, 40.07645259,  1.43682734,\n",
+       "        -1.43294754],\n",
+       "       [-2.03936077,  9.49443222, 85.2487593 , 40.04411781,  1.43565736,\n",
+       "        -1.43325785],\n",
+       "       [-2.04186187,  9.48955805, 85.17933142, 40.0116929 ,  1.43448413,\n",
+       "        -1.43356904],\n",
+       "       ...,\n",
+       "       [-3.90720611,  5.85436487, 33.39945635, 15.82893159,  0.5594767 ,\n",
+       "        -1.66565038],\n",
+       "       [-3.91623795,  5.83676359, 33.14874276, 15.71184079,  0.55523999,\n",
+       "        -1.6667741 ],\n",
+       "       [-3.92518186,  5.81933364, 32.90046971, 15.5958898 ,  0.55104453,\n",
+       "        -1.66788688]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inv_test_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test RMSE: 0.217\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[900:2100,5], label='true')\n",
+    "plt.plot(inv_forecast_y[900:2100,5], label='pre')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_squared_error: 0.0011780920849826654\n",
+      "mean_absolute_error: 0.013530156512489254\n",
+      "rmse: 0.03432334606332351\n",
+      "r2 score: 0.9966738024269023\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[900:2100], inv_forecast_y[900:2100]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "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('xin99939高频re_test(t+3).csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame(inv_forecast_y[:,5], 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('xin99939高频re_forecast(t+3).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
index ce34f6b..dd9c4b7 100644
--- a/iceemdan信号重构.ipynb
+++ b/iceemdan信号重构.ipynb
@@ -2,20 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from math import sqrt\n",
     "from numpy import concatenate\n",
@@ -36,64 +25,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 10,
    "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",
+    "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\\单多步可视化\\icial模型\\t+1\\xin9999低频_forecast(T+1).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,
+   "execution_count": 11,
    "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",
+    "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\\单多步可视化\\icial模型\\t+1\\xin99939高频re_forecast(t+1).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,
+   "execution_count": 12,
    "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",
+    "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\\单多步可视化\\icial模型\\t+1\\xin9999低频_test(T+1).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,
+   "execution_count": 13,
    "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",
+    "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\\单多步可视化\\icial模型\\t+1\\xin99939高频re_test(t+1).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,
+   "execution_count": 14,
    "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",
+    "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\\单多步可视化\\icial模型\\t+1\\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,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -123,23 +112,23 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4.607052</td>\n",
+       "      <td>4.679599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4.585928</td>\n",
+       "      <td>4.675801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>4.574285</td>\n",
+       "      <td>4.636000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>4.546298</td>\n",
+       "      <td>4.572200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4.497329</td>\n",
+       "      <td>4.525266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -147,23 +136,23 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1557</th>\n",
-       "      <td>0.022011</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1558</th>\n",
-       "      <td>0.020204</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1559</th>\n",
-       "      <td>0.019515</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1560</th>\n",
-       "      <td>0.022857</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1561</th>\n",
-       "      <td>0.018875</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -172,34 +161,138 @@
       ],
       "text/plain": [
        "      column_name\n",
-       "0        4.607052\n",
-       "1        4.585928\n",
-       "2        4.574285\n",
-       "3        4.546298\n",
-       "4        4.497329\n",
+       "0        4.679599\n",
+       "1        4.675801\n",
+       "2        4.636000\n",
+       "3        4.572200\n",
+       "4        4.525266\n",
        "...           ...\n",
-       "1557     0.022011\n",
-       "1558     0.020204\n",
-       "1559     0.019515\n",
-       "1560     0.022857\n",
-       "1561     0.018875\n",
+       "1557     0.000000\n",
+       "1558     0.000000\n",
+       "1559     0.000000\n",
+       "1560     0.000000\n",
+       "1561     0.000000\n",
        "\n",
        "[1562 rows x 1 columns]"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pre_data=f_low+f_high\n",
-    "pre_data"
+    "true_2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>column_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.681645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.683139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.684575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.685911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.687193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1557</th>\n",
+       "      <td>1.545752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1558</th>\n",
+       "      <td>1.545890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1559</th>\n",
+       "      <td>1.546023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1560</th>\n",
+       "      <td>1.546150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1561</th>\n",
+       "      <td>1.546288</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1562 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      column_name\n",
+       "0        1.681645\n",
+       "1        1.683139\n",
+       "2        1.684575\n",
+       "3        1.685911\n",
+       "4        1.687193\n",
+       "...           ...\n",
+       "1557     1.545752\n",
+       "1558     1.545890\n",
+       "1559     1.546023\n",
+       "1560     1.546150\n",
+       "1561     1.546288\n",
+       "\n",
+       "[1562 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_low"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -253,23 +346,23 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1557</th>\n",
-       "      <td>6.661338e-16</td>\n",
+       "      <td>2.220446e-16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1558</th>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>-4.440892e-16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1559</th>\n",
-       "      <td>2.220446e-16</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1560</th>\n",
-       "      <td>2.220446e-16</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1561</th>\n",
-       "      <td>4.440892e-16</td>\n",
+       "      <td>-4.440892e-16</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -284,57 +377,79 @@
        "3     4.572200e+00\n",
        "4     4.525266e+00\n",
        "...            ...\n",
-       "1557  6.661338e-16\n",
-       "1558  0.000000e+00\n",
-       "1559  2.220446e-16\n",
-       "1560  2.220446e-16\n",
-       "1561  4.440892e-16\n",
+       "1557  2.220446e-16\n",
+       "1558 -4.440892e-16\n",
+       "1559  0.000000e+00\n",
+       "1560  0.000000e+00\n",
+       "1561 -4.440892e-16\n",
        "\n",
        "[1562 rows x 1 columns]"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "pre_data=f_low+f_high\n",
+    "pre_data\n",
     "true=true_low+true_high\n",
-    "true"
+    "true\n",
+    "# df1 = pd.DataFrame(pre_data, columns=['column_name'])\n",
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "# df1.to_csv('(t+3)经过ICEEMDAN分解预测的预测集.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4.6702959]\n",
+      " [4.6353927]\n",
+      " [4.6446364]\n",
+      " ...\n",
+      " [0.       ]\n",
+      " [0.       ]\n",
+      " [0.       ]]\n"
+     ]
     }
    ],
    "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()"
+    "import numpy as np\n",
+    "\n",
+    "def update_pre_based_on_true(true, pre):\n",
+    "    # 确保 true 和 pre 是 NumPy 数组\n",
+    "    true = np.array(true)\n",
+    "    pre = np.array(pre)\n",
+    "    \n",
+    "    # 使用布尔索引将 pre 中对应位置的值设为0\n",
+    "    pre[true == 0] = 0\n",
+    "    \n",
+    "    return pre\n",
+    "\n",
+    "\n",
+    "\n",
+    "updated_pre = update_pre_based_on_true(true_2, pre_data)\n",
+    "print(updated_pre)\n",
+    "df1 = pd.DataFrame(updated_pre, columns=['column_name'])\n",
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df1.to_csv('(t+1)经过ICEEMDAN分解预测的预测集.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -346,7 +461,7 @@
    "source": [
     "plt.figure(figsize=(16,8))\n",
     "plt.plot(true_2, label='true')\n",
-    "plt.plot(pre_data, label='pre')\n",
+    "plt.plot(updated_pre, label='pre')\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -361,17 +476,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 24,
    "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"
+      "mean_squared_error: 0.0018723911403168718\n",
+      "mean_absolute_error: 0.015867955050384196\n",
+      "rmse: 0.04488873526226243\n",
+      "r2 score: 0.9994400158090125\n"
      ]
     }
    ],
@@ -382,22 +497,442 @@
     "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('mean_squared_error:', mean_squared_error(updated_pre, true))  # mse)\n",
+    "print(\"mean_absolute_error:\", mean_absolute_error(updated_pre, true))  # mae\n",
     "print(\"rmse:\", sqrt(mean_squared_error(pre_data, true)))\n",
-    "print(\"r2 score:\", r2_score(pre_data[:], true[:]))#"
+    "print(\"r2 score:\", r2_score(updated_pre, true))#"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true3=true_2[150:400]\n",
+    "pre_data3=updated_pre[150:400]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 假设true_2和updated_pre是你的NumPy数组\n",
+    "true3 = true_2[150:400]\n",
+    "pre_data3 = updated_pre[150:400]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pre_data3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
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+       "       4.10600042e+00, 4.18293333e+00, 4.21853399e+00, 4.25460005e+00,\n",
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+       "       4.60559940e+00, 4.53426600e+00, 4.50513315e+00, 4.49013329e+00,\n",
+       "       4.41386700e+00, 4.45499992e+00, 4.37726641e+00, 4.41346645e+00,\n",
+       "       4.34786653e+00, 4.34599972e+00, 4.29546642e+00, 4.27073288e+00,\n",
+       "       4.22719955e+00, 4.16773415e+00, 4.10826588e+00, 4.07379961e+00,\n",
+       "       4.01966667e+00, 3.95240021e+00, 3.91913366e+00, 3.82226634e+00,\n",
+       "       3.74139977e+00, 3.67379951e+00, 3.63913369e+00, 3.54620004e+00,\n",
+       "       3.46993303e+00, 3.41440010e+00, 3.34759975e+00, 3.23346663e+00,\n",
+       "       3.16666675e+00, 3.09766650e+00, 3.01139998e+00, 2.92106676e+00,\n",
+       "       2.82239986e+00, 2.73320007e+00, 2.61460018e+00, 2.51999998e+00,\n",
+       "       2.42013359e+00, 2.31419992e+00, 2.20986652e+00, 2.09686661e+00,\n",
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+       "       1.54139996e+00, 1.42546666e+00, 1.30626667e+00, 1.19073319e+00,\n",
+       "       1.07273352e+00, 9.58733380e-01, 8.42133343e-01, 7.15199947e-01,\n",
+       "       5.81333339e-01, 4.45266664e-01, 3.14866692e-01, 2.03333318e-01,\n",
+       "       1.25466660e-01, 8.83999990e-02, 6.87333350e-02, 4.97333260e-02,\n",
+       "       3.25333250e-02, 1.56666660e-02, 2.40000000e-03, 1.33333000e-04,\n",
+       "       6.67000000e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 假设df是你的DataFrame\n",
+    "true31 = true3['column_name'].to_numpy()\n",
+    "true31"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6,6))\n",
+    "plt.plot(true31, label='true')\n",
+    "plt.plot(pre_data3, label='pre')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame(updated_pre[150:400], columns=['column_name'])\n",
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df1.to_csv('1天的经过ICEEMDAN分解预测的预测集1.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame(true[150:400], columns=['column_name'])\n",
+    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
+    "df2.to_csv('1天的经过ICEEMDAN分解预测的真实集.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(1562, 1)\n"
+      "(10415, 1)\n"
      ]
     }
    ],
@@ -405,20 +940,20 @@
     "# 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",
+    "pre = scaler.fit_transform(updated_pre)\n",
     "print(pre.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(1562, 1)\n"
+      "(10415, 1)\n"
      ]
     }
    ],
@@ -431,17 +966,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 38,
    "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"
+      "mean_squared_error: 0.04675641413227651\n",
+      "mean_absolute_error: 0.0798491015148862\n",
+      "rmse: 0.21689357303163628\n",
+      "r2 score: 0.9912435196234671\n"
      ]
     }
    ],
@@ -452,19 +987,12 @@
     "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))"
+    "print('mean_squared_error:', mean_squared_error(updated_pre, true))  # mse)\n",
+    "print(\"mean_absolute_error:\", mean_absolute_error(updated_pre, true))  # mae\n",
+    "print(\"rmse:\", sqrt(mean_squared_error(pre_data, true)))\n",
+    "print(\"r2 score:\", r2_score(pre[900:2100], true_data[900:2100]))#"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,