{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:38.202021Z", "start_time": "2023-05-06T08:51:32.964932Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "E:\\Pycharm\\Anaconda3\\lib\\site-packages\\requests\\__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.15) or chardet (3.0.4) doesn't match a supported version!\n", " warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n" ] } ], "source": [ "# 调用相关库\n", "import os # 导入os模块，用于操作系统功能，比如环境变量\n", "import math # 导入math模块，提供基本的数学功能\n", "import pandas as pd # 导入pandas模块，用于数据处理和分析\n", "from math import sqrt # 从math模块导入sqrt函数，用于计算平方根\n", "from numpy import concatenate # 从numpy模块导入concatenate函数，用于数组拼接\n", "import matplotlib.pyplot as plt # 导入matplotlib.pyplot模块，用于绘图\n", "import numpy as np # 导入numpy模块，用于数值计算\n", "import tensorflow as tf # 导入tensorflow模块，用于深度学习\n", "from sklearn.preprocessing import MinMaxScaler # 导入sklearn中的MinMaxScaler，用于特征缩放\n", "from sklearn.preprocessing import StandardScaler # 导入sklearn中的StandardScaler，用于特征标准化\n", "from sklearn.preprocessing import LabelEncoder # 导入sklearn中的LabelEncoder，用于标签编码\n", "from sklearn.metrics import mean_squared_error # 导入sklearn中的mean_squared_error，用于计算均方误差\n", "from tensorflow.keras.layers import * # 从tensorflow.keras.layers导入所有层，用于构建神经网络\n", "from tensorflow.keras.models import * # 从tensorflow.keras.models导入所有模型，用于构建和管理模型\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error,r2_score # 导入额外的评估指标\n", "from pandas import DataFrame # 从pandas导入DataFrame，用于创建和操作数据表\n", "from pandas import concat # 从pandas导入concat函数，用于DataFrame的拼接\n", "import keras.backend as K # 导入keras的后端接口\n", "from scipy.io import savemat, loadmat # 从scipy.io导入savemat和loadmat，用于MATLAB文件的读写\n", "from sklearn.neural_network import MLPRegressor # 从sklearn.neural_network导入MLPRegressor，用于创建多层感知器回归模型\n", "from keras.callbacks import LearningRateScheduler # 从keras.callbacks导入LearningRateScheduler，用于调整学习率\n", "from tensorflow.keras import Input, Model, Sequential # 从tensorflow.keras导入Input, Model和Sequential，用于模型构建\n", "from keras.layers import Dense, Activation, Dropout, LSTM, LayerNormalization, Input\n", "# 从keras.layers模块导入多个层类。\n", "# Dense是全连接层，用于构建神经网络。\n", "# Activation是激活层，用于应用激活函数。\n", "# Dropout是丢弃层，用于减少过拟合。\n", "# LSTM是长短时记忆网络层，用于处理序列数据。\n", "# LayerNormalization是层归一化，用于标准化层的输出。\n", "# Input是输入层，用于定义模型输入的形状。\n", "from tensorflow.keras.models import Model\n", "# 从tensorflow.keras.models模块导入Model类。\n", "# Model是用于构建和训练深度学习模型的基类。\n", "from sklearn.model_selection import KFold\n", "# 从sklearn.model_selection模块导入KFold类。\n", "# KFold是一种交叉验证方法，用于评估模型的泛化能力。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:38.217931Z", "start_time": "2023-05-06T08:51:38.203021Z" } }, "outputs": [], "source": [ "# 构建时间序列特征集\n", "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n", " # 定义一个函数，将时间序列数据转换为监督学习数据格式\n", " n_vars = 1 if type(data) is list else data.shape[1]\n", " # 根据输入数据的类型（列表或其他）来决定变量的数量\n", " df = DataFrame(data)\n", " # 将数据转换为pandas DataFrame\n", " cols, names = list(), list()\n", " # 初始化两个列表，用于存储数据列和列名\n", "\n", " # input sequence (t-n, ... t-1)\n", " for i in range(n_in, 0, -1):\n", " # 创建输入序列\n", " cols.append(df.shift(i))\n", " # 将DataFrame向下移动i个单位，生成序列\n", " names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]\n", " # 生成列名，表示每个时刻的变量\n", "\n", " # forecast sequence (t, t+1, ... t+n)\n", " for i in range(0, n_out):\n", " # 创建输出序列（预测值）\n", " cols.append(df.shift(-i))\n", " # 将DataFrame向上移动i个单位，生成序列\n", " if i == 0:\n", " names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]\n", " else:\n", " names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]\n", " # 生成列名，表示预测的未来时刻的变量\n", "\n", " # put it all together\n", " agg = concat(cols, axis=1)\n", " # 将所有生成的列合并成一个新的DataFrame\n", " agg.columns = names\n", " # 设置新DataFrame的列名\n", "\n", " # drop rows with NaN values\n", " if dropnan:\n", " agg.dropna(inplace=True)\n", " # 如果dropnan为真，则删除含有NaN的行\n", "\n", " return agg\n", " # 返回处理后的数据" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:40.590996Z", "start_time": "2023-05-06T08:51:38.219931Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " imf0 imf1 imf2\n", "0 3531.751521 -191.760606 -97.465751\n", "1 3531.473729 -191.684118 -97.693419\n", "2 3531.227653 -191.210280 -97.722481\n", "3 3530.660421 -190.706280 -97.838212\n", "4 3529.466722 -190.488685 -98.200846\n", "... ... ... ...\n", "5401 1400.221164 -285.714281 -40.909098\n", "5402 1398.327887 -289.990072 -43.032448\n", "5403 1396.429155 -293.765904 -45.520341\n", "5404 1395.016313 -296.474920 -47.544327\n", "5405 1394.130730 -298.027741 -48.828768\n", "\n", "[5406 rows x 3 columns]\n" ] } ], "source": [ "# 加载数据集\n", "dataset = pd.read_excel(\"VMD.xlsx\")\n", "print(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 最后一行vmd分解的数据少一行 删除最后一行数据" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:40.607000Z", "start_time": "2023-05-06T08:51:40.591997Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " imf0 imf1 imf2\n", "0 3531.751521 -191.760606 -97.465751\n", "1 3531.473729 -191.684118 -97.693419\n", "2 3531.227653 -191.210280 -97.722481\n", "3 3530.660421 -190.706280 -97.838212\n", "4 3529.466722 -190.488685 -98.200846\n", "... ... ... ...\n", "5401 1400.221164 -285.714281 -40.909098\n", "5402 1398.327887 -289.990072 -43.032448\n", "5403 1396.429155 -293.765904 -45.520341\n", "5404 1395.016313 -296.474920 -47.544327\n", "5405 1394.130730 -298.027741 -48.828768\n", "\n", "[5406 rows x 3 columns]\n" ] } ], "source": [ "dataset=dataset.dropna()#直接删除含有缺失值的行\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:40.623003Z", "start_time": "2023-05-06T08:51:40.608000Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1 2 3 4 \\\n", "imf0 3531.751521 3531.473729 3531.227653 3530.660421 3529.466722 \n", "imf1 -191.760606 -191.684118 -191.210280 -190.706280 -190.488685 \n", "imf2 -97.465751 -97.693419 -97.722481 -97.838212 -98.200846 \n", "\n", " 5 6 7 8 9 ... \\\n", "imf0 3527.299925 3525.499968 3524.157672 3522.674701 3520.788146 ... \n", "imf1 -190.884944 -190.458168 -189.068413 -187.310603 -185.418466 ... \n", "imf2 -98.811120 -97.600851 -93.964879 -88.228244 -80.318451 ... \n", "\n", " 5396 5397 5398 5399 5400 \\\n", "imf0 1402.198237 1401.787920 1401.785531 1401.852845 1401.488442 \n", "imf1 -265.490849 -270.460561 -274.530030 -278.073661 -281.637131 \n", "imf2 -44.490206 -43.928264 -42.449575 -40.843035 -40.071165 \n", "\n", " 5401 5402 5403 5404 5405 \n", "imf0 1400.221164 1398.327887 1396.429155 1395.016313 1394.130730 \n", "imf1 -285.714281 -289.990072 -293.765904 -296.474920 -298.027741 \n", "imf2 -40.909098 -43.032448 -45.520341 -47.544327 -48.828768 \n", "\n", "[3 rows x 5406 columns]\n" ] } ], "source": [ "dataset=dataset.T #转置\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:40.639006Z", "start_time": "2023-05-06T08:51:40.624003Z" } }, "outputs": [], "source": [ "u = dataset.values\n", "# 'dataset' 是一个Pandas DataFrame。\n", "# '.values' 属性用于提取DataFrame中的数据，并将其转换为一个NumPy数组。\n", "# 这个数组被赋值给变量 'u'。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:41.316894Z", "start_time": "2023-05-06T08:51:41.273884Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 发布日期 收盘指数 开盘指数 最高指数 最低指数 成交量(亿股) 成交额(亿元) \\\n", "0 2022/5/20 0:00 3259.34 3223.16 3267.41 3220.70 25.14 243.93 \n", "1 2022/5/19 0:00 3234.45 3211.60 3265.00 3208.24 25.61 254.88 \n", "2 2022/5/18 0:00 3262.52 3255.59 3280.32 3245.07 21.21 204.77 \n", "3 2022/5/17 0:00 3267.97 3301.25 3301.25 3255.96 26.54 268.17 \n", "4 2022/5/16 0:00 3305.64 3241.51 3311.60 3221.92 30.75 301.98 \n", "... ... ... ... ... ... ... ... \n", "5401 2000/1/7 0:00 1106.19 1076.06 1117.71 1058.83 0.65 8.75 \n", "5402 2000/1/6 0:00 1065.67 1025.38 1070.79 1011.00 0.30 4.21 \n", "5403 2000/1/5 0:00 1028.87 1035.47 1057.85 1013.07 0.25 2.90 \n", "5404 2000/1/4 0:00 1027.66 1001.98 1035.36 985.47 0.14 1.83 \n", "5405 1999/12/30 0:00 1000.00 1000.00 1000.00 1000.00 0.10 1.24 \n", "\n", " 涨跌幅(%) \n", "0 0.77 \n", "1 -0.86 \n", "2 -0.17 \n", "3 -1.14 \n", "4 3.09 \n", "... ... \n", "5401 3.80 \n", "5402 3.58 \n", "5403 0.12 \n", "5404 2.77 \n", "5405 0.00 \n", "\n", "[5406 rows x 8 columns]\n" ] } ], "source": [ "dataset1=pd.read_csv(\"农林牧渔.csv\", encoding='gbk')\n", "# 使用pandas模块的read_csv函数读取名为\"农林牧渔.csv\"的文件。\n", "# 参数'encoding'设置为'gbk'，这通常用于读取中文字符，确保文件中的中文字符能够正确读取。\n", "# 读取的数据被存储在名为'dataset'的DataFrame变量中。\n", "print(dataset1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:41.977920Z", "start_time": "2023-05-06T08:51:41.957915Z" } }, "outputs": [], "source": [ "data=dataset1.values[:,2:] #加载特征数据" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T08:51:43.651020Z", "start_time": "2023-05-06T08:51:43.639017Z" } }, "outputs": [], "source": [ "from tensorflow.keras.regularizers import l2\n", "# 从tensorflow.keras.regularizers导入l2，用于应用L2正则化。\n", "\n", "def implement_lstm(train_X, train_y, l2_penalty=0.01):\n", " # 定义implement_lstm函数，接收训练数据train_X, train_y和L2正则化系数l2_penalty。\n", "\n", " inputs = Input(shape=(train_X.shape[1], train_X.shape[2]))\n", " # 创建模型的输入层，指定输入数据的形状。\n", " # train_X.shape[1]是时间步长，train_X.shape[2]是特征数量。\n", "\n", " lstm = LSTM(128, activation='selu', return_sequences=False, kernel_regularizer=l2(l2_penalty))(inputs)\n", " # 添加一个LSTM层，设置128个神经元，激活函数为selu。\n", " # return_sequences=False意味着LSTM层输出的是最后一个时间步的输出。\n", " # kernel_regularizer=l2(l2_penalty)应用L2正则化以防止过拟合。\n", "\n", " outputs = Dense(train_y.shape[1])(lstm)\n", " # 添加一个全连接层，神经元数量与train_y的特征数相同。\n", "\n", " model = Model(inputs=inputs, outputs=outputs)\n", " # 创建一个模型，指定输入和输出层。\n", "\n", " model.compile(loss='mse', optimizer='adam')\n", " # 编译模型，使用均方误差作为损失函数，优化器为Adam。\n", "\n", " model.summary()\n", " # 打印模型摘要信息。\n", "\n", " return model\n", " # 返回构建的模型。\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T09:10:19.140111Z", "start_time": "2023-05-06T08:57:29.814911Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n", "Model: \"model\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_1 (InputLayer) [(None, 12, 7)] 0 \n", " \n", " lstm (LSTM) (None, 128) 69632 \n", " \n", " dense (Dense) (None, 6) 774 \n", " \n", "=================================================================\n", "Total params: 70,406\n", "Trainable params: 70,406\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Epoch 1/10\n", "102/102 - 3s - loss: 0.1276 - val_loss: 0.1220 - 3s/epoch - 28ms/step\n", "Epoch 2/10\n", "102/102 - 2s - loss: 0.0329 - val_loss: 0.0596 - 2s/epoch - 15ms/step\n", "Epoch 3/10\n", "102/102 - 2s - loss: 0.0185 - val_loss: 0.0471 - 2s/epoch - 17ms/step\n", "Epoch 4/10\n", "102/102 - 2s - loss: 0.0122 - val_loss: 0.0517 - 2s/epoch - 19ms/step\n", "Epoch 5/10\n", "102/102 - 2s - loss: 0.0088 - val_loss: 0.0394 - 2s/epoch - 20ms/step\n", "Epoch 6/10\n", "102/102 - 2s - loss: 0.0068 - val_loss: 0.0214 - 2s/epoch - 19ms/step\n", "Epoch 7/10\n", "102/102 - 2s - loss: 0.0052 - val_loss: 0.0115 - 2s/epoch - 19ms/step\n", "Epoch 8/10\n", "102/102 - 2s - loss: 0.0062 - val_loss: 0.0169 - 2s/epoch - 19ms/step\n", "Epoch 9/10\n", "102/102 - 2s - loss: 0.0033 - val_loss: 0.0255 - 2s/epoch - 20ms/step\n", "Epoch 10/10\n", "102/102 - 2s - loss: 0.0028 - val_loss: 0.0190 - 2s/epoch - 20ms/step\n", "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n", "Model: \"model_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_2 (InputLayer) [(None, 12, 7)] 0 \n", " \n", " lstm_1 (LSTM) (None, 128) 69632 \n", " \n", " dense_1 (Dense) (None, 6) 774 \n", " \n", "=================================================================\n", "Total params: 70,406\n", "Trainable params: 70,406\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Epoch 1/10\n", "102/102 - 3s - loss: 0.2488 - val_loss: 0.1620 - 3s/epoch - 26ms/step\n", "Epoch 2/10\n", "102/102 - 2s - loss: 0.1540 - val_loss: 0.1237 - 2s/epoch - 18ms/step\n", "Epoch 3/10\n", "102/102 - 2s - loss: 0.0904 - val_loss: 0.1235 - 2s/epoch - 20ms/step\n", "Epoch 4/10\n", "102/102 - 2s - loss: 0.0668 - val_loss: 0.1164 - 2s/epoch - 20ms/step\n", "Epoch 5/10\n", "102/102 - 2s - loss: 0.0668 - val_loss: 0.0550 - 2s/epoch - 19ms/step\n", "Epoch 6/10\n", "102/102 - 2s - loss: 0.0398 - val_loss: 0.0575 - 2s/epoch - 20ms/step\n", "Epoch 7/10\n", "102/102 - 2s - loss: 0.0293 - val_loss: 0.0422 - 2s/epoch - 20ms/step\n", "Epoch 8/10\n", "102/102 - 2s - loss: 0.0243 - val_loss: 0.0571 - 2s/epoch - 19ms/step\n", "Epoch 9/10\n", "102/102 - 2s - loss: 0.0333 - val_loss: 0.0363 - 2s/epoch - 19ms/step\n", "Epoch 10/10\n", "102/102 - 2s - loss: 0.0215 - val_loss: 0.0484 - 2s/epoch - 19ms/step\n", "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n", "Model: \"model_2\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_3 (InputLayer) [(None, 12, 7)] 0 \n", " \n", " lstm_2 (LSTM) (None, 128) 69632 \n", " \n", " dense_2 (Dense) (None, 6) 774 \n", " \n", "=================================================================\n", "Total params: 70,406\n", "Trainable params: 70,406\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Epoch 1/10\n", "102/102 - 3s - loss: 0.5086 - val_loss: 0.2938 - 3s/epoch - 26ms/step\n", "Epoch 2/10\n", "102/102 - 2s - loss: 0.2868 - val_loss: 0.2285 - 2s/epoch - 19ms/step\n", "Epoch 3/10\n", "102/102 - 2s - loss: 0.2504 - val_loss: 0.2616 - 2s/epoch - 20ms/step\n", "Epoch 4/10\n", "102/102 - 2s - loss: 0.2013 - val_loss: 0.1862 - 2s/epoch - 20ms/step\n", "Epoch 5/10\n", "102/102 - 2s - loss: 0.1923 - val_loss: 0.1663 - 2s/epoch - 20ms/step\n", "Epoch 6/10\n", "102/102 - 2s - loss: 0.1521 - val_loss: 0.2175 - 2s/epoch - 20ms/step\n", "Epoch 7/10\n", "102/102 - 2s - loss: 0.1343 - val_loss: 0.1691 - 2s/epoch - 20ms/step\n", "Epoch 8/10\n", "102/102 - 2s - loss: 0.2005 - val_loss: 0.1381 - 2s/epoch - 20ms/step\n", "Epoch 9/10\n", "102/102 - 2s - loss: 0.1288 - val_loss: 0.0888 - 2s/epoch - 19ms/step\n", "Epoch 10/10\n", "102/102 - 2s - loss: 0.1090 - val_loss: 0.0794 - 2s/epoch - 20ms/step\n" ] } ], "source": [ "prediction_test = []\n", "actual_test = []\n", "# 初始化两个空列表，用于存储后续的预测值和实际值。\n", "\n", "for imf in u:\n", " # 遍历u中的每个内在模态函数（IMF）。\n", "\n", " imf = imf.reshape(-1, 1)\n", " # 将当前的IMF重塑为二维数组，以便进行后续操作。\n", "\n", " imf = pd.DataFrame(imf)\n", " # 将重塑后的IMF转换为pandas DataFrame。\n", "\n", " data_1 = pd.DataFrame(data)\n", " # 将其他特征（假设存储在变量data中）转换为pandas DataFrame。\n", "\n", " data_ = pd.concat((imf, data_1), axis=1)\n", " # 将IMF和其他特征合并为一个DataFrame，IMF作为新的列添加到data_1的前面。\n", "\n", " data_ = data_.values\n", " # 将合并后的DataFrame转换为NumPy数组，以进行后续的数值操作。\n", "\n", " data_ = data_.astype('float32')\n", " # 将数组中的数据类型转换为float32，这是为了确保数据类型的一致性，特别是在进行深度学习或其他数值计算时。\n", "\n", " \n", " # 构建成监督学习问题\n", " n_in=12 #输入步数\n", " n_out=6 #输出步数\n", " n_vars=data_.shape[1]# 获取values数组的列数，即变量的数量。\n", " # 构建成监督学习问题\n", " reframed = series_to_supervised(data_, n_in, n_out) \n", "# 使用series_to_supervised函数将数据转换为监督学习格式。\n", "# values是要转换的数据，n_in是输入序列的长度，n_out是输出序列的长度。\n", "# 此函数返回一个新的DataFrame，其中包含用于训练监督学习模型所需的输入和输出数据。\n", "\n", " # 取出保留的变量\n", " contain_vars = []\n", "# 初始化一个空列表，用于存储需要保留的列名。\n", "\n", " for i in range(1, n_in+1):\n", " contain_vars += [('var%d(t-%d)' % (j, i)) for j in range(1, n_vars+1)]\n", " # 遍历从1到n_in的数字，每次迭代都会生成一组列名。\n", " # 这些列名表示过去n_in个时间步的每个变量（比如var1(t-1), var2(t-1), ..., var1(t-n_in), var2(t-n_in)等）。\n", "\n", " data3 = reframed[contain_vars + ['var1(t)'] + [('var1(t+%d)' % (j)) for j in range(1, n_out)]]\n", "# 从reframed数据框中提取特定的列。\n", "# 'contain_vars'包含了输入序列需要的所有列。\n", "# ['var1(t)'] 是当前时间步的第一个变量。\n", "# [('var1(t+%d)' % (j)) for j in range(1, n_out)] 生成了输出序列的列名，即预测未来n_out-1个时间步的第一个变量。\n", " values = data3.values# 将前面处理好的数据转换成numpy数组，方便后续的数据操作\n", " n_train_hours = int(values.shape[0]*0.8) # 80%训练集\n", " train = values[:n_train_hours, :] # 从values数组中取出前n_train_hours行作为训练集。\n", " test = values[n_train_hours:, :] # 从values数组中取出剩下的行作为测试集。\n", " # 归一化\n", " scaler = StandardScaler()\n", " train = scaler.fit_transform(train)# 对训练集数据进行标准化\n", " test = scaler.fit_transform(test)# 对测试集数据进行标准化。\n", " # 把数据分为输入和输出\n", " train_X, train_y = train[:, :n_in*n_vars], train[:, n_in*n_vars:]\n", " test_X, test_y = test[:, :n_in*n_vars], test[:, n_in*n_vars:]\n", " # 把输入重塑成3D格式 [样例，时间步， 特征]\n", " train_X = train_X.reshape((train_X.shape[0], n_in, n_vars))\n", " test_X = test_X.reshape((test_X.shape[0],n_in, n_vars))\n", " def scheduler(epoch): # 定义一个学习率调度器，根据epoch调整学习率。\n", " # 如果当前epoch是10的倍数且不为0，则调整学习率\n", " if epoch % 10 == 0 and epoch != 0:\n", " lr = K.get_value(tmp.optimizer.lr)# 获取当前的学习率\n", " K.set_value(tmp.optimizer.lr, lr * 0.5)# 将学习率设置为当前值的一半\n", " print(\"lr changed to {}\".format(lr * 0.5))# 打印新的学习率值\n", " return K.get_value(tmp.optimizer.lr)# 返回新的学习率值\n", " reduce_lr = LearningRateScheduler(scheduler)# 创建一个学习率调度器对象，将上面定义的scheduler函数作为参数\n", " tmp = implement_lstm(train_X, train_y)# 使用implement_lstm函数构建LSTM模型\n", " kf = KFold(n_splits=4)# 创建一个KFold对象，用于交叉验证，分为4个部分\n", " history = tmp.fit(train_X, train_y, batch_size=32, epochs=10, validation_split=0.25, verbose=2)\n", "# 训练模型，使用指定的批大小、epoch数、验证集比例和详细程度\n", " # 作出预测\n", " yhat = tmp.predict(test_X)# 使用训练好的模型对测试集进行预测\n", " yhat=yhat.reshape(-1,1)# 重塑预测结果为二维数组\n", "# 反向缩放预测值 测试集\n", " yhat = np.repeat(yhat, n_in * n_vars + n_out, axis=-1)\n", "# 重复扩展预测值数组，以匹配逆变换的维度要求。\n", " inv_yhat = scaler.inverse_transform(np.reshape(yhat, (len(yhat), n_in * n_vars + n_out)))[:,0]\n", "# 使用之前的缩放器对象进行逆变换，恢复预测值到原始尺度。\n", " inv_yhat = inv_yhat.reshape(-1, n_out)\n", "# 重塑预测结果。\n", "\n", " prediction_test.append(inv_yhat)\n", "# 将逆变换后的预测值添加到prediction_test列表。\n", "\n", "# 反向缩放实际值 测试集\n", " test_y = test_y.reshape(-1,1)\n", "# 重塑测试集的目标值为二维数组。\n", " y = np.repeat(test_y, n_in * n_vars + n_out, axis=-1)\n", "# 重复扩展测试集目标值数组，以匹配逆变换的维度要求。\n", " inv_y = scaler.inverse_transform(np.reshape(y, (len(test_y), n_in * n_vars + n_out)))[:,0]\n", "# 使用之前的缩放器对象进行逆变换，恢复实际值到原始尺度。\n", " inv_y = inv_y.reshape(-1, n_out)\n", "# 重塑实际结果。\n", "\n", " actual_test.append(inv_y)\n", "# 将逆变换后的实际值添加到actual_test列表。\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([623.65045, 618.6924 , 603.57495, 614.0135 , 604.22705, 621.57104],\n", " dtype=float32), array([625.2488 , 619.08453, 605.4806 , 616.36816, 608.02045, 624.40753],\n", " dtype=float32), array([633.2138 , 627.0779 , 615.2496 , 625.77216, 617.76245, 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dtype=float32), array([1129.2041, 1139.2174, 1156.3223, 1143.6376, 1138.238 , 1121.4867],\n", " dtype=float32), array([1123.5496, 1130.2614, 1157.3856, 1144.7831, 1150.3201, 1136.3354],\n", " dtype=float32), array([1123.7814, 1132.9285, 1163.9531, 1156.0215, 1163.4993, 1155.0306],\n", " dtype=float32), array([1135.4496, 1145.0807, 1178.9718, 1173.6451, 1182.7661, 1175.022 ],\n", " dtype=float32), array([1148.0859, 1159.2137, 1193.8881, 1190.5717, 1200.4279, 1191.2415],\n", " dtype=float32)]\n" ] } ], "source": [ "mean_pre_test = [] \n", "# 初始化一个空列表 mean_pre_test，用于存储每个时间点的预测值总和。\n", "\n", "for i in range(0, len(prediction_test[0])):\n", " # 遍历预测结果的每个时间点。\n", " sum = 0\n", " # 初始化变量 sum，用于累加当前时间点的所有预测值。\n", "\n", " for j in range(0, len(prediction_test)):\n", " # 内层循环，遍历所有预测结果。\n", " sum = sum + prediction_test[j][i]\n", " # 将当前时间点的预测值累加到 sum。\n", "\n", " # mean = sum / len(prediction_test) \n", " # 这一行被注释掉了，本应用于计算平均值，但目前不执行。\n", "\n", " mean_pre_test.append(sum)\n", " # 将累加后的总和添加到 mean_pre_test 列表。\n", "\n", "print(mean_pre_test)\n", "# 打印 mean_pre_test 列表，显示每个时间点的预测值总和。\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([585.435 , 587.11584, 589.3555 , 591.2902 , 592.60706, 593.3793 ],\n", " dtype=float32), array([587.63904, 589.8859 , 591.8278 , 593.15454, 593.94006, 594.04236],\n", " dtype=float32), array([590.4207 , 592.3691 , 593.70135, 594.4952 , 594.6086 , 594.3888 ],\n", " dtype=float32), array([592.9149 , 594.25214, 595.0499 , 595.1693 , 594.95953, 594.5946 ],\n", " dtype=float32), array([594.8075 , 595.6087 , 595.7299 , 595.525 , 595.16956, 594.9381 ],\n", " dtype=float32), array([596.17224, 596.2949 , 596.09076, 595.73956, 595.5177 , 596.3485 ],\n", " dtype=float32), array([596.8649 , 596.66113, 596.3101 , 596.0928 , 596.9364 , 598.764 ],\n", " dtype=float32), array([597.23663, 596.88544, 596.6685 , 597.51996, 599.3635 , 602.2754 ],\n", " dtype=float32), 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1133.5002, 1121.9751],\n", " dtype=float32), array([1178.523 , 1161.1478, 1145.7279, 1131.6245, 1120.0546, 1111.9347],\n", " dtype=float32), array([1159.4631, 1143.9406, 1129.7428, 1118.106 , 1109.9542, 1104.467 ],\n", " dtype=float32), array([1142.1729, 1127.8751, 1116.1515, 1107.9447, 1102.4364, 1099.8883],\n", " dtype=float32), array([1126.0287, 1114.212 , 1105.9297, 1100.3761, 1097.8185, 1097.481 ],\n", " dtype=float32), array([1112.2944, 1103.9297, 1098.3097, 1095.7184, 1095.3812, 1095.7697],\n", " dtype=float32), array([1101.9525, 1096.2588, 1093.6119, 1093.2505, 1093.6447, 1092.7805],\n", " dtype=float32), array([1094.231 , 1091.5209, 1091.113 , 1091.4879, 1090.6283, 1086.805 ],\n", " dtype=float32), array([1089.4529, 1088.9906, 1089.3239, 1088.4441, 1084.6184, 1078.7408],\n", " dtype=float32), array([1086.8911, 1087.1746, 1086.2521, 1082.3997, 1076.5162, 1070.786 ],\n", " dtype=float32), array([1085.0483, 1084.0746, 1080.1727, 1074.2587, 1068.5265, 1064.7916],\n", " dtype=float32), array([1081.9202, 1077.9608, 1071.9933, 1066.2335, 1062.5063, 1061.1566],\n", " dtype=float32)]\n" ] } ], "source": [ "mean_actual_test = []\n", "# 初始化一个空列表 mean_actual_test，用于存储每个时间点的真实值总和。\n", "\n", "for i in range(0, len(actual_test[0])):\n", " # 遍历真实结果的每个时间点。\n", " sum = 0\n", " # 初始化变量 sum，用于累加当前时间点的所有真实值。\n", "\n", " for j in range(0, len(actual_test)):\n", " # 内层循环，遍历所有真实结果。\n", " sum = sum + actual_test[j][i]\n", " # 将当前时间点的真实值累加到 sum。\n", "\n", " # mean = sum / len(actual_test)\n", " # 这一行被注释掉了，本应用于计算平均值，但目前不执行。\n", "\n", " mean_actual_test.append(sum)\n", " # 将累加后的总和添加到 mean_actual_test 列表。\n", "\n", "print(mean_actual_test)\n", "# 打印 mean_actual_test 列表，显示每个时间点的真实值总和。\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def mape(y_true, y_pred):\n", " # 定义一个计算平均绝对百分比误差（MAPE）的函数。\n", " record = []\n", " for index in range(len(y_true)):\n", " # 遍历实际值和预测值。\n", " if abs(y_true[index]) > 10:\n", " # 如果实际值的绝对值大于10，则计算MAPE。\n", " temp_mape = np.abs((y_pred[index] - y_true[index]) / y_true[index])\n", " # 计算单个预测的MAPE。\n", " record.append(temp_mape)\n", " # 将MAPE添加到记录列表中。\n", " return np.mean(record) * 100\n", " # 返回所有记录的平均值，乘以100得到百分比。\n", "\n", "def evaluate_forecasts(test, forecasts, n_out):\n", " # 定义一个函数来评估预测的性能。\n", " rmse_dic = {}\n", " mae_dic = {}\n", " mape_dic = {}\n", " r2_dic = {}\n", " # 初始化存储各个评估指标的字典。\n", "\n", " for i in range(n_out):\n", " # 遍历每一个预测步长。\n", " actual = [float(row[i]) for row in test]\n", " # 从测试集中提取实际值。\n", " predicted = [float(forecast[i]) for forecast in forecasts]\n", " # 从预测结果中提取预测值。\n", "\n", " rmse = sqrt(mean_squared_error(actual, predicted))\n", " # 计算均方根误差（RMSE）。\n", " rmse_dic['t+' + str(i+1) + ' RMSE'] = rmse\n", " # 将RMSE结果添加到字典中。\n", "\n", " mae = mean_absolute_error(actual, predicted)\n", " # 计算平均绝对误差（MAE）。\n", " mae_dic['t+' + str(i+1) + ' MAE'] = mae\n", " # 将MAE结果添加到字典中。\n", "\n", " mape_ = mape(actual, predicted)\n", " # 计算平均绝对百分比误差（MAPE）。\n", " mape_dic['t+' + str(i+1) + ' MAPE'] = mape_\n", " # 将MAPE结果添加到字典中。\n", "\n", " r2 = r2_score(actual, predicted)\n", " # 计算R平方值（R2）。\n", " r2_dic['t+' + str(i+1) + ' R2'] = r2\n", " # 将R2结果添加到字典中。\n", "\n", " return rmse_dic, mae_dic, mape_dic, r2_dic\n", " # 返回包含所有评估指标的字典。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "dic_list = []\n", "# 初始化一个空列表，用于存储评估结果。\n", "\n", "dic = evaluate_forecasts(mean_actual_test, mean_pre_test, n_out)\n", "# 调用evaluate_forecasts函数。\n", "# 此函数将计算每个预测步长的RMSE、MAE、MAPE和R2值。\n", "\n", "dic_list.append(dic)\n", "# 将评估结果（一个包含四种评估指标的字典）添加到dic_list列表中。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[({'t+1 RMSE': 33.77693092445425, 't+2 RMSE': 39.11441558946967, 't+3 RMSE': 42.33294930368944, 't+4 RMSE': 42.32774089827625, 't+5 RMSE': 46.687650731330415, 't+6 RMSE': 45.23881249913675}, {'t+1 MAE': 28.063076323613608, 't+2 MAE': 31.09749443323139, 't+3 MAE': 32.60468384242014, 't+4 MAE': 33.49820121807601, 't+5 MAE': 35.85342378917121, 't+6 MAE': 35.81221775486651}, {'t+1 MAPE': 2.447889483415708, 't+2 MAPE': 2.5304907053316383, 't+3 MAPE': 2.599171923595281, 't+4 MAPE': 2.8499105367921826, 't+5 MAPE': 3.020994645321792, 't+6 MAPE': 3.113077874960882}, {'t+1 R2': 0.9929718071129047, 't+2 R2': 0.990562772730015, 't+3 R2': 0.9889315345808368, 't+4 R2': 0.9889202511313333, 't+5 R2': 0.9865034641578266, 't+6 R2': 0.9873127198487927})]\n" ] } ], "source": [ "print(dic_list)#显示预测指标数值" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T09:10:46.646049Z", "start_time": "2023-05-06T09:10:46.632046Z" } }, "outputs": [], "source": [ "actual = [float(row[0]) for row in mean_actual_test]\n", "# 从反向缩放后的实际值集合(mean_actual_test)中提取每个样本的第一个值。\n", "# 这个列表包含了对应于时间步长t的所有实际值。\n", "\n", "predicted_1st = [float(forecast[0]) for forecast in mean_pre_test]\n", "# 从反向缩放后的预测值集合(mean_actual_test)中提取每个样本的第一个预测值。\n", "# 这个列表包含了模型对于时间步长t+1的所有预测值。\n", "\n", "predicted_3st = [float(forecast[2]) for forecast in mean_pre_test]\n", "# 从反向缩放后的预测值集合(mean_actual_test)中提取每个样本的第三个预测值。\n", "# 这个列表包含了模型对于时间步长t+3的所有预测值。\n", "\n", "predicted_6st = [float(forecast[5]) for forecast in mean_pre_test]\n", "# 从反向缩放后的预测值集合(mean_actual_test)中提取每个样本的第六个预测值。\n", "# 这个列表包含了模型对于时间步长t+6的所有预测值。\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2023-05-06T09:15:48.619770Z", "start_time": "2023-05-06T09:15:47.162804Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 2167, "width": 3242 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "# 导入seaborn库，用于数据可视化。\n", "\n", "%config InlineBackend.figure_format = 'retina'\n", "# 设置Jupyter笔记本中的图形格式为高分辨率的'retina'格式。\n", "\n", "plt.rcParams['font.sans-serif']=['Simhei'] #解决画图中文不显示问题\n", "# 设置matplotlib的默认字体为Simhei，以解决中文显示问题。\n", "\n", "plt.rcParams['axes.unicode_minus'] = False\n", "# 设置matplotlib的配置，用来正常显示负号。\n", "\n", "plt.figure(figsize=(6,4),dpi=600)\n", "# 创建一个图形对象，并设置大小为6x4英寸，分辨率为600dpi。\n", "\n", "x = range(1, len(actual) + 1)\n", "# 创建x轴的值，从1到实际值列表的长度。\n", "\n", "plt.xticks(x[::500])\n", "# 设置x轴的刻度，每500个点显示一个刻度。\n", "\n", "plt.tick_params(labelsize=10) # 改变刻度字体大小\n", "# 设置刻度标签的字体大小。\n", "\n", "plt.plot(x, actual, linestyle=\"--\", linewidth=1, label='Real')\n", "# 绘制实际值的折线图，线型为虚线，线宽为1，标签为'Real'。\n", "\n", "plt.plot(x, predicted_1st, linestyle=\"--\", linewidth=1, label='1step')\n", "# 绘制一步预测值的折线图，线型为虚线，线宽为1，标签为'1step'。\n", "\n", "plt.plot(x, predicted_3st, linestyle=\"--\", linewidth=1, label='3step')\n", "# 绘制三步预测值的折线图，线型为虚线，线宽为1，标签为'3step'。\n", "\n", "plt.plot(x, predicted_6st, linestyle=\"--\", linewidth=1, label='6step')\n", "# 绘制六步预测值的折线图，线型为虚线，线宽为1，标签为'6step'。\n", "\n", "plt.rcParams.update({'font.size': 10}) # 改变图例里面的字体大小\n", "# 更新图例的字体大小。\n", "\n", "plt.legend(loc='upper right', frameon=False)\n", "# 显示图例，位置在图形的右上角，没有边框。\n", "\n", "plt.xlabel(\"样本点\", fontsize=10)\n", "# 设置x轴标签为\"样本点\"，字体大小为10。\n", "\n", "plt.ylabel(\"值\", fontsize=10)\n", "# 设置y轴标签为\"值\"，字体大小为10。\n", "\n", "# plt.xlim(xmin=600, xmax=700) # 显示600-1000的值 局部放大有利于观察\n", "# 如果需要，可以取消注释这行代码，以局部放大显示600到700之间的值。\n", "\n", "# plt.savefig('figure/预测结果图.png')\n", "# 如果需要，可以取消注释这行代码，以将图形保存为PNG文件。\n", "\n", "plt.show()\n", "# 显示图形。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 5 }