From 7b9191a9a47fe8ae6f27c4fce48fbe8183c99cba Mon Sep 17 00:00:00 2001
From: hanyp <hanyp@qibebt.ac.cn>
Date: Mon, 12 Aug 2024 07:40:16 +0800
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20'iceemdan-low-LSTM.ipynb'?=
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-{
- "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": [
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-      "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": [
-    {
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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# # 假设你已经有了原始数据和重构数据\n",
-    "# # 原始数据\n",
-    "original_data = data1['Power'].values\n",
-    "\n",
-    "# # 创建时间序列（假设时间序列与数据对应）\n",
-    "time = range(len(original_data))\n",
-    "\n",
-    "# # 创建画布和子图\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "# # 绘制原始数据\n",
-    "# plt.plot(time, original_data, label='Original Data', color='blue')\n",
-    "\n",
-    "# # 绘制重构数据\n",
-    "plt.plot( data_re[:], label='Reconstructed Data', color='red')\n",
-    "\n",
-    "# # 添加标题和标签\n",
-    "plt.title('Comparison between Original and reconstructed_data_high')\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel('Power')\n",
-    "plt.legend()\n",
-    "\n",
-    "# # 显示图形\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data3=data1.iloc[:,:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Temp   Humidity       GHI       DHI  Rainfall  column_name\n",
-      "0       19.779453  40.025826  3.232706  1.690531       0.0     1.426824\n",
-      "1       19.714937  39.605961  3.194991  1.576346       0.0     1.426819\n",
-      "2       19.549330  39.608631  3.070866  1.576157       0.0     1.426815\n",
-      "3       19.405870  39.680702  3.038623  1.482489       0.0     1.426812\n",
-      "4       19.387363  39.319881  2.656474  1.134153       0.0     1.426810\n",
-      "...           ...        ...       ...       ...       ...          ...\n",
-      "104251  13.303740  34.212711  1.210789  0.787026       0.0     1.629381\n",
-      "104252  13.120920  34.394939  2.142980  1.582670       0.0     1.629328\n",
-      "104253  12.879215  35.167400  1.926214  1.545889       0.0     1.629271\n",
-      "104254  12.915867  35.359989  1.317695  0.851529       0.0     1.629213\n",
-      "104255  13.134816  34.500034  1.043269  0.597816       0.0     1.629152\n",
-      "\n",
-      "[104256 rows x 6 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "# # 创建data3和imf1_array对应的DataFrame\n",
-    "data3_df = pd.DataFrame(data3)\n",
-    "imf1_df = pd.DataFrame(data_re)\n",
-    "\n",
-    "# # 合并data3_df和imf1_df\n",
-    "merged_df = pd.concat([data3_df, imf1_df], axis=1)\n",
-    "\n",
-    "# # 设置行数为35040行\n",
-    "merged_df = merged_df.iloc[:104256]\n",
-    "\n",
-    "# # 打印合并后的表\n",
-    "print(merged_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(104256, 6)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(104256, 6)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 使用MinMaxScaler进行归一化\n",
-    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
-    "scaledData1 = scaler.fit_transform(merged_df)\n",
-    "print(scaledData1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            0         1         2         3    4         5   0(t-96)  \\\n",
-      "96   0.555631  0.349673  0.190042  0.040558  0.0  0.836699  0.490360   \n",
-      "97   0.564819  0.315350  0.211335  0.044613  0.0  0.836762  0.489088   \n",
-      "98   0.576854  0.288321  0.229657  0.047549  0.0  0.836826  0.485824   \n",
-      "99   0.581973  0.268243  0.247775  0.053347  0.0  0.836891  0.482997   \n",
-      "100  0.586026  0.264586  0.266058  0.057351  0.0  0.836956  0.482632   \n",
-      "\n",
-      "      1(t-96)   2(t-96)   3(t-96)  ...    2(t-1)    3(t-1)  4(t-1)    5(t-1)  \\\n",
-      "96   0.369105  0.002088  0.002013  ...  0.166009  0.036794     0.0  0.836635   \n",
-      "97   0.364859  0.002061  0.001839  ...  0.190042  0.040558     0.0  0.836699   \n",
-      "98   0.364886  0.001973  0.001839  ...  0.211335  0.044613     0.0  0.836762   \n",
-      "99   0.365615  0.001950  0.001697  ...  0.229657  0.047549     0.0  0.836826   \n",
-      "100  0.361965  0.001679  0.001167  ...  0.247775  0.053347     0.0  0.836891   \n",
-      "\n",
-      "       0(t+1)    1(t+1)    2(t+1)    3(t+1)  4(t+1)    5(t+1)  \n",
-      "96   0.564819  0.315350  0.211335  0.044613     0.0  0.836762  \n",
-      "97   0.576854  0.288321  0.229657  0.047549     0.0  0.836826  \n",
-      "98   0.581973  0.268243  0.247775  0.053347     0.0  0.836891  \n",
-      "99   0.586026  0.264586  0.266058  0.057351     0.0  0.836956  \n",
-      "100  0.590772  0.258790  0.282900  0.060958     0.0  0.837022  \n",
-      "\n",
-      "[5 rows x 588 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_steps_in =96 #历史时间长度\n",
-    "n_steps_out=1#预测时间长度\n",
-    "processedData1 = time_series_to_supervised(scaledData1,n_steps_in,n_steps_out)\n",
-    "print(processedData1.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# processedData1.to_csv('processedData1.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_x = processedData1.loc[:,'0(t-96)':'5(t-1)']\n",
-    "data_y = processedData1.loc[:,'5']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(104159, 576)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "96        0.836699\n",
-       "97        0.836762\n",
-       "98        0.836826\n",
-       "99        0.836891\n",
-       "100       0.836956\n",
-       "            ...   \n",
-       "104250    0.989547\n",
-       "104251    0.989508\n",
-       "104252    0.989466\n",
-       "104253    0.989423\n",
-       "104254    0.989378\n",
-       "Name: 5, Length: 104159, dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(104159,)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_y.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(83328, 96, 6) (83328,) (20831, 96, 6) (20831,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 7.划分训练集和测试集\n",
-    "\n",
-    "test_size = int(len(data_x) * 0.2)\n",
-    "# 计算训练集和测试集的索引范围\n",
-    "train_indices = range(len(data_x) - test_size)\n",
-    "test_indices = range(len(data_x) - test_size, len(data_x))\n",
-    "\n",
-    "# 根据索引范围划分数据集\n",
-    "train_X1 = data_x.iloc[train_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
-    "test_X1 = data_x.iloc[test_indices].values.reshape((-1, n_steps_in, scaledData1.shape[1]))\n",
-    "train_y = data_y.iloc[train_indices].values\n",
-    "test_y = data_y.iloc[test_indices].values\n",
-    "\n",
-    "\n",
-    "# # 多次运行代码时希望得到相同的数据分割，可以设置 random_state 参数为一个固定的整数值\n",
-    "# train_X1,test_X1, train_y, test_y = train_test_split(data_x.values, data_y.values, test_size=0.2, random_state=343)\n",
-    "# reshape input to be 3D [samples, timesteps, features]\n",
-    "train_X = train_X1.reshape((train_X1.shape[0], n_steps_in, scaledData1.shape[1]))\n",
-    "test_X = test_X1.reshape((test_X1.shape[0], n_steps_in,scaledData1.shape[1]))\n",
-    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)\n",
-    "# 使用train_test_split函数划分训练集和测试集，测试集的比重是40%。\n",
-    "# 然后将train_X1、test_X1进行一个升维，变成三维，维数分别是[samples,timesteps,features]。\n",
-    "# 打印一下他们的shape：\\\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(83328, 96, 6)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_X1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "d:\\Anaconda3\\lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-      "  super().__init__(**kwargs)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">69,120</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m69,120\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,249</span> (270.50 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m69,249\u001b[0m (270.50 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,249</span> (270.50 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m69,249\u001b[0m (270.50 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from keras.layers import GRU, Bidirectional\n",
-    "from keras.models import Model\n",
-    "from keras.layers import Input, Conv1D, MaxPooling1D, LSTM, Dense, Attention, Flatten\n",
-    "import keras\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import LSTM, Dense\n",
-    "\n",
-    "# 创建模型\n",
-    "model = Sequential()\n",
-    "\n",
-    "# 添加单层 LSTM\n",
-    "model.add(LSTM(units=128, input_shape=(96, 6)))\n",
-    "\n",
-    "# 添加输出层\n",
-    "model.add(Dense(1))\n",
-    "\n",
-    "# 编译模型\n",
-    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
-    "\n",
-    "# 查看模型结构\n",
-    "model.summary()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m122s\u001b[0m 92ms/step - loss: 0.0156 - val_loss: 1.0318e-05\n",
-      "Epoch 2/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 85ms/step - loss: 1.2280e-05 - val_loss: 2.9811e-06\n",
-      "Epoch 3/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 87ms/step - loss: 9.1935e-06 - val_loss: 2.5579e-06\n",
-      "Epoch 4/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.0443e-05 - val_loss: 8.4623e-06\n",
-      "Epoch 5/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 90ms/step - loss: 1.1108e-05 - val_loss: 8.1167e-06\n",
-      "Epoch 6/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 85ms/step - loss: 5.3451e-06 - val_loss: 2.4689e-06\n",
-      "Epoch 7/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 90ms/step - loss: 1.5962e-05 - val_loss: 2.2134e-06\n",
-      "Epoch 8/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 5.3290e-06 - val_loss: 3.5285e-07\n",
-      "Epoch 9/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 97ms/step - loss: 4.5184e-06 - val_loss: 1.2596e-07\n",
-      "Epoch 10/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.6976e-06 - val_loss: 7.1095e-06\n",
-      "Epoch 11/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m123s\u001b[0m 95ms/step - loss: 6.6386e-06 - val_loss: 1.0392e-07\n",
-      "Epoch 12/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 97ms/step - loss: 2.3165e-06 - val_loss: 8.4822e-07\n",
-      "Epoch 13/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 92ms/step - loss: 3.5823e-06 - val_loss: 4.9285e-08\n",
-      "Epoch 14/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 90ms/step - loss: 3.1791e-06 - val_loss: 2.2294e-07\n",
-      "Epoch 15/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 2.9977e-06 - val_loss: 3.9852e-06\n",
-      "Epoch 16/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 2.3874e-06 - val_loss: 1.3594e-07\n",
-      "Epoch 17/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 103ms/step - loss: 3.1801e-07 - val_loss: 1.6932e-07\n",
-      "Epoch 18/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 98ms/step - loss: 1.5647e-06 - val_loss: 2.1397e-08\n",
-      "Epoch 19/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 104ms/step - loss: 1.4188e-06 - val_loss: 1.4569e-07\n",
-      "Epoch 20/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 99ms/step - loss: 1.1043e-06 - val_loss: 5.9704e-07\n",
-      "Epoch 21/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 103ms/step - loss: 2.0067e-06 - val_loss: 2.0218e-06\n",
-      "Epoch 22/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 111ms/step - loss: 1.9982e-06 - val_loss: 2.2618e-07\n",
-      "Epoch 23/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 104ms/step - loss: 1.4178e-06 - val_loss: 1.3009e-06\n",
-      "Epoch 24/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 100ms/step - loss: 2.7170e-06 - val_loss: 1.2247e-08\n",
-      "Epoch 25/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 95ms/step - loss: 1.8664e-06 - val_loss: 5.6499e-07\n",
-      "Epoch 26/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 68ms/step - loss: 1.3434e-06 - val_loss: 1.2509e-08\n",
-      "Epoch 27/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 65ms/step - loss: 1.8632e-06 - val_loss: 5.3179e-07\n",
-      "Epoch 28/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 63ms/step - loss: 1.2746e-06 - val_loss: 9.0354e-08\n",
-      "Epoch 29/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 63ms/step - loss: 1.5440e-06 - val_loss: 1.2604e-07\n",
-      "Epoch 30/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 68ms/step - loss: 1.2646e-06 - val_loss: 2.5639e-07\n",
-      "Epoch 31/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 62ms/step - loss: 1.3377e-06 - val_loss: 4.0479e-08\n",
-      "Epoch 32/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 37ms/step - loss: 7.9140e-07 - val_loss: 1.1824e-06\n",
-      "Epoch 33/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 37ms/step - loss: 2.1865e-06 - val_loss: 4.2140e-06\n",
-      "Epoch 34/100\n",
-      "\u001b[1m1302/1302\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 39ms/step - loss: 1.4884e-06 - val_loss: 1.8359e-06\n",
-      "\u001b[1m651/651\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compile and train the model\n",
-    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
-    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
-    "\n",
-    "# 定义早停机制\n",
-    "early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min')\n",
-    "\n",
-    "# 拟合模型，并添加早停机制和模型检查点\n",
-    "history = model.fit(train_X, train_y, epochs=100, batch_size=64, validation_data=(test_X, test_y), \n",
-    "                    callbacks=[early_stopping])\n",
-    "# 预测\n",
-    "lstm_pred = model.predict(test_X)\n",
-    "# 将预测结果的形状修改为与原始数据相同的形状"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20831, 1)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lstm_pred.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20831,)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_y.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_y1=test_y.reshape(20831,1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.65620206],\n",
-       "       [0.6565139 ],\n",
-       "       [0.65682633],\n",
-       "       ...,\n",
-       "       [0.98946626],\n",
-       "       [0.98942303],\n",
-       "       [0.98937795]])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_y1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results1 = np.broadcast_to(lstm_pred, (20831, 6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_y2 = np.broadcast_to(test_y1, (20831, 6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 反归一化\n",
-    "inv_forecast_y = scaler.inverse_transform(results1)\n",
-    "inv_test_y = scaler.inverse_transform(test_y2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2.81937911e+01, 6.84129659e+01, 9.24487326e+02, 4.31993807e+02,\n",
-       "        1.56176147e+01, 1.19628092e+00],\n",
-       "       [2.82096130e+01, 6.84437997e+01, 9.24926524e+02, 4.32198926e+02,\n",
-       "        1.56250366e+01, 1.19668613e+00],\n",
-       "       [2.82254649e+01, 6.84746920e+01, 9.25366555e+02, 4.32404434e+02,\n",
-       "        1.56324725e+01, 1.19709211e+00],\n",
-       "       ...,\n",
-       "       [4.51026009e+01, 1.01364948e+02, 1.39385702e+03, 6.51203592e+02,\n",
-       "        2.35493057e+01, 1.62932764e+00],\n",
-       "       [4.51004072e+01, 1.01360673e+02, 1.39379613e+03, 6.51175153e+02,\n",
-       "        2.35482767e+01, 1.62927146e+00],\n",
-       "       [4.50981204e+01, 1.01356216e+02, 1.39373265e+03, 6.51145506e+02,\n",
-       "        2.35472040e+01, 1.62921289e+00]])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inv_test_y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test RMSE: 0.002\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 计算均方根误差\n",
-    "rmse = sqrt(mean_squared_error(inv_test_y[:,5], inv_forecast_y[:,5]))\n",
-    "print('Test RMSE: %.3f' % rmse)\n",
-    "#画图\n",
-    "plt.figure(figsize=(16,8))\n",
-    "plt.plot(inv_test_y[:,5], label='true')\n",
-    "plt.plot(inv_forecast_y[:,5], label='pre')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean_squared_error: 1.8358609523038586e-06\n",
-      "mean_absolute_error: 0.0012240899816947145\n",
-      "rmse: 0.0013549394644425479\n",
-      "r2 score: 0.9998451201868883\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
-    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
-    "from math import sqrt\n",
-    "from sklearn.metrics import mean_absolute_error\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from sklearn.metrics import r2_score\n",
-    "print('mean_squared_error:', mean_squared_error(lstm_pred, test_y))  # mse)\n",
-    "print(\"mean_absolute_error:\", mean_absolute_error(lstm_pred, test_y))  # mae\n",
-    "print(\"rmse:\", sqrt(mean_squared_error(lstm_pred,test_y)))\n",
-    "print(\"r2 score:\", r2_score(inv_test_y[:], inv_forecast_y[:]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.DataFrame(inv_test_y[:,5], columns=['column_name'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
-    "df1.to_csv('低频_test.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2 = pd.DataFrame(inv_forecast_y[:,5], columns=['column_name'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
-    "df2.to_csv('低频_forecast.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}