3091 lines
1.3 MiB
Plaintext
3091 lines
1.3 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-06T08:51:38.202021Z",
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"start_time": "2023-05-06T08:51:32.964932Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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" warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n"
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]
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}
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],
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"source": [
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"# 调用相关库\n",
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"import os # 导入os模块，用于操作系统功能，比如环境变量\n",
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"import math # 导入math模块，提供基本的数学功能\n",
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"import pandas as pd # 导入pandas模块，用于数据处理和分析\n",
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"from math import sqrt # 从math模块导入sqrt函数，用于计算平方根\n",
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"from numpy import concatenate # 从numpy模块导入concatenate函数，用于数组拼接\n",
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"import matplotlib.pyplot as plt # 导入matplotlib.pyplot模块，用于绘图\n",
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"import numpy as np # 导入numpy模块，用于数值计算\n",
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"import tensorflow as tf # 导入tensorflow模块，用于深度学习\n",
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"from sklearn.preprocessing import MinMaxScaler # 导入sklearn中的MinMaxScaler，用于特征缩放\n",
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"from sklearn.preprocessing import StandardScaler # 导入sklearn中的StandardScaler，用于特征标准化\n",
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"from sklearn.preprocessing import LabelEncoder # 导入sklearn中的LabelEncoder，用于标签编码\n",
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"from sklearn.metrics import mean_squared_error # 导入sklearn中的mean_squared_error，用于计算均方误差\n",
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"from tensorflow.keras.layers import * # 从tensorflow.keras.layers导入所有层，用于构建神经网络\n",
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"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",
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"from sklearn.metrics import mean_squared_error, mean_absolute_error,r2_score # 导入额外的评估指标\n",
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"from pandas import DataFrame # 从pandas导入DataFrame，用于创建和操作数据表\n",
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"from pandas import concat # 从pandas导入concat函数，用于DataFrame的拼接\n",
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"import keras.backend as K # 导入keras的后端接口\n",
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"from scipy.io import savemat, loadmat # 从scipy.io导入savemat和loadmat，用于MATLAB文件的读写\n",
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"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",
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"from keras.callbacks import LearningRateScheduler # 从keras.callbacks导入LearningRateScheduler，用于调整学习率\n",
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"from tensorflow.keras import Input, Model, Sequential # 从tensorflow.keras导入Input, Model和Sequential，用于模型构建\n",
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"from keras.layers import Dense, Activation, Dropout, LSTM, LayerNormalization, Input\n",
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"# 从keras.layers模块导入多个层类。\n",
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"# Dense是全连接层，用于构建神经网络。\n",
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"# Activation是激活层，用于应用激活函数。\n",
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"# Dropout是丢弃层，用于减少过拟合。\n",
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"# LSTM是长短时记忆网络层，用于处理序列数据。\n",
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"# LayerNormalization是层归一化，用于标准化层的输出。\n",
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"# Input是输入层，用于定义模型输入的形状。\n",
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"from tensorflow.keras.models import Model\n",
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"# 从tensorflow.keras.models模块导入Model类。\n",
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"# Model是用于构建和训练深度学习模型的基类。\n",
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"from sklearn.model_selection import KFold\n",
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"# 从sklearn.model_selection模块导入KFold类。\n",
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"# KFold是一种交叉验证方法，用于评估模型的泛化能力。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-06T08:51:38.217931Z",
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"start_time": "2023-05-06T08:51:38.203021Z"
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}
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},
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"outputs": [],
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"source": [
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"# 构建时间序列特征集\n",
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"def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
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" # 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",
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" n_vars = 1 if type(data) is list else data.shape[1]\n",
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" # 根据输入数据的类型（列表或其他）来决定变量的数量\n",
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" df = DataFrame(data)\n",
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" # 将数据转换为pandas DataFrame\n",
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" cols, names = list(), list()\n",
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" # 初始化两个列表，用于存储数据列和列名\n",
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"\n",
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" # input sequence (t-n, ... t-1)\n",
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" for i in range(n_in, 0, -1):\n",
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" # 创建输入序列\n",
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" cols.append(df.shift(i))\n",
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" # 将DataFrame向下移动i个单位，生成序列\n",
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" names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]\n",
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" # 生成列名，表示每个时刻的变量\n",
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"\n",
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" # forecast sequence (t, t+1, ... t+n)\n",
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" for i in range(0, n_out):\n",
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" # 创建输出序列（预测值）\n",
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" cols.append(df.shift(-i))\n",
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" # 将DataFrame向上移动i个单位，生成序列\n",
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" if i == 0:\n",
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" names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]\n",
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" else:\n",
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" names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]\n",
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" # 生成列名，表示预测的未来时刻的变量\n",
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"\n",
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" # put it all together\n",
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" agg = concat(cols, axis=1)\n",
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" # 将所有生成的列合并成一个新的DataFrame\n",
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" agg.columns = names\n",
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" # 设置新DataFrame的列名\n",
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"\n",
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" # drop rows with NaN values\n",
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" if dropnan:\n",
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" agg.dropna(inplace=True)\n",
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" # 如果dropnan为真，则删除含有NaN的行\n",
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"\n",
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" return agg\n",
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" # 返回处理后的数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-06T08:51:40.590996Z",
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"start_time": "2023-05-06T08:51:38.219931Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" imf0 imf1 imf2\n",
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"0 3531.751521 -191.760606 -97.465751\n",
|
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"1 3531.473729 -191.684118 -97.693419\n",
|
|||
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"2 3531.227653 -191.210280 -97.722481\n",
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|||
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"3 3530.660421 -190.706280 -97.838212\n",
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"4 3529.466722 -190.488685 -98.200846\n",
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"... ... ... ...\n",
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"5401 1400.221164 -285.714281 -40.909098\n",
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"5402 1398.327887 -289.990072 -43.032448\n",
|
|||
|
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"5403 1396.429155 -293.765904 -45.520341\n",
|
|||
|
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"5404 1395.016313 -296.474920 -47.544327\n",
|
|||
|
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"5405 1394.130730 -298.027741 -48.828768\n",
|
|||
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"\n",
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"[5406 rows x 3 columns]\n"
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]
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}
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],
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"source": [
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"# 加载数据集\n",
|
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"dataset = pd.read_excel(\"VMD.xlsx\")\n",
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"print(dataset)"
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]
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|||
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},
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|||
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{
|
|||
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"cell_type": "markdown",
|
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"metadata": {},
|
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"source": [
|
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|
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"### 最后一行vmd分解的数据少一行 删除最后一行数据"
|
|||
|
|
]
|
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|
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},
|
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{
|
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"cell_type": "code",
|
|||
|
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"execution_count": 4,
|
|||
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"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2023-05-06T08:51:40.607000Z",
|
|||
|
|
"start_time": "2023-05-06T08:51:40.591997Z"
|
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}
|
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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|
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" imf0 imf1 imf2\n",
|
|||
|
|
"0 3531.751521 -191.760606 -97.465751\n",
|
|||
|
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"1 3531.473729 -191.684118 -97.693419\n",
|
|||
|
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"2 3531.227653 -191.210280 -97.722481\n",
|
|||
|
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"3 3530.660421 -190.706280 -97.838212\n",
|
|||
|
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"4 3529.466722 -190.488685 -98.200846\n",
|
|||
|
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"... ... ... ...\n",
|
|||
|
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"5401 1400.221164 -285.714281 -40.909098\n",
|
|||
|
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"5402 1398.327887 -289.990072 -43.032448\n",
|
|||
|
|
"5403 1396.429155 -293.765904 -45.520341\n",
|
|||
|
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"5404 1395.016313 -296.474920 -47.544327\n",
|
|||
|
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"5405 1394.130730 -298.027741 -48.828768\n",
|
|||
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"\n",
|
|||
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"[5406 rows x 3 columns]\n"
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]
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}
|
|||
|
|
],
|
|||
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"source": [
|
|||
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|
"dataset=dataset.dropna()#直接删除含有缺失值的行\n",
|
|||
|
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"print(dataset)"
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|||
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]
|
|||
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},
|
|||
|
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{
|
|||
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"cell_type": "code",
|
|||
|
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"execution_count": 5,
|
|||
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"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2023-05-06T08:51:40.623003Z",
|
|||
|
|
"start_time": "2023-05-06T08:51:40.608000Z"
|
|||
|
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}
|
|||
|
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},
|
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"outputs": [
|
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|
{
|
|||
|
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"name": "stdout",
|
|||
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"output_type": "stream",
|
|||
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"text": [
|
|||
|
|
" 0 1 2 3 4 \\\n",
|
|||
|
|
"imf0 3531.751521 3531.473729 3531.227653 3530.660421 3529.466722 \n",
|
|||
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"imf1 -191.760606 -191.684118 -191.210280 -190.706280 -190.488685 \n",
|
|||
|
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"imf2 -97.465751 -97.693419 -97.722481 -97.838212 -98.200846 \n",
|
|||
|
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"\n",
|
|||
|
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" 5 6 7 8 9 ... \\\n",
|
|||
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"imf0 3527.299925 3525.499968 3524.157672 3522.674701 3520.788146 ... \n",
|
|||
|
|
"imf1 -190.884944 -190.458168 -189.068413 -187.310603 -185.418466 ... \n",
|
|||
|
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"imf2 -98.811120 -97.600851 -93.964879 -88.228244 -80.318451 ... \n",
|
|||
|
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"\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"
|
|||
|
|
}
|
|||
|
|
},
|
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|
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"outputs": [
|
|||
|
|
{
|
|||
|
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"name": "stdout",
|
|||
|
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"output_type": "stream",
|
|||
|
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"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",
|
|||
|
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"1 2022/5/19 0:00 3234.45 3211.60 3265.00 3208.24 25.61 254.88 \n",
|
|||
|
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"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, 633.55927],\n",
|
|||
|
|
" dtype=float32), array([636.1861 , 630.284 , 619.28485, 629.82544, 622.0204 , 637.3502 ],\n",
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
" dtype=float32), array([655.5188 , 651.9247 , 641.55273, 656.1473 , 646.4649 , 662.7194 ],\n",
|
|||
|
|
" dtype=float32), array([664.2362 , 659.55414, 650.2051 , 664.6214 , 655.0185 , 671.5304 ],\n",
|
|||
|
|
" dtype=float32), array([666.24805, 660.6218 , 650.4562 , 664.84204, 655.0235 , 671.67194],\n",
|
|||
|
|
" dtype=float32), array([663.9518 , 660.1682 , 648.52057, 661.75226, 655.646 , 672.50586],\n",
|
|||
|
|
" dtype=float32), array([675.79193, 672.6672 , 662.1785 , 676.29083, 669.6863 , 685.59406],\n",
|
|||
|
|
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|
|||
|
|
" dtype=float32), array([677.1524 , 675.3202 , 664.1226 , 676.20465, 670.24475, 685.8749 ],\n",
|
|||
|
|
" dtype=float32), array([688.6037 , 687.45996, 677.2477 , 690.0032 , 680.3793 , 694.6529 ],\n",
|
|||
|
|
" dtype=float32), array([695.93146, 695.1808 , 684.571 , 696.87933, 685.9596 , 700.2399 ],\n",
|
|||
|
|
" dtype=float32), array([706.38696, 705.3889 , 694.45874, 706.83826, 694.40265, 708.4253 ],\n",
|
|||
|
|
" dtype=float32), array([707.8538, 707.3474, 694.4205, 706.0059, 693.4234, 706.4618],\n",
|
|||
|
|
" dtype=float32), array([711.3512 , 709.6993 , 695.50104, 704.636 , 691.5268 , 704.224 ],\n",
|
|||
|
|
" dtype=float32), array([704.5342 , 701.25104, 685.93164, 692.89624, 678.0666 , 691.5998 ],\n",
|
|||
|
|
" dtype=float32), array([699.3338 , 695.3271 , 680.02484, 687.49774, 670.94586, 685.41394],\n",
|
|||
|
|
" dtype=float32), array([701.8734 , 697.85236, 682.519 , 690.449 , 675.90497, 691.7468 ],\n",
|
|||
|
|
" dtype=float32), array([707.9105 , 704.7721 , 689.614 , 696.5685 , 686.01404, 702.7405 ],\n",
|
|||
|
|
" dtype=float32), array([705.93225, 704.2679 , 688.5622 , 696.32 , 685.8843 , 700.98456],\n",
|
|||
|
|
" dtype=float32), array([708.1857, 706.9787, 691.1082, 699.1819, 691.4646, 705.4971],\n",
|
|||
|
|
" dtype=float32), array([715.43756, 714.3114 , 699.62006, 708.398 , 701.40063, 714.87463],\n",
|
|||
|
|
" dtype=float32), array([718.7063 , 717.85596, 703.48004, 712.1675 , 704.55646, 715.8771 ],\n",
|
|||
|
|
" dtype=float32), array([717.3259 , 716.8602 , 702.5728 , 711.8793 , 704.7095 , 716.36066],\n",
|
|||
|
|
" dtype=float32), array([717.7212 , 716.995 , 701.3704 , 712.33875, 703.796 , 716.504 ],\n",
|
|||
|
|
" dtype=float32), array([714.59686, 712.5029 , 696.16156, 707.7064 , 698.6679 , 713.5057 ],\n",
|
|||
|
|
" dtype=float32), array([711.1265 , 710.5703 , 692.7643 , 705.9341 , 695.85425, 709.71387],\n",
|
|||
|
|
" dtype=float32), array([713.8114 , 714.1371 , 696.25854, 710.19635, 699.9887 , 711.3999 ],\n",
|
|||
|
|
" dtype=float32), array([718.46124, 718.6967 , 701.5881 , 715.3829 , 704.8107 , 714.7584 ],\n",
|
|||
|
|
" dtype=float32), array([718.2692 , 718.09674, 701.8456 , 713.7712 , 704.6913 , 715.47363],\n",
|
|||
|
|
" dtype=float32), array([711.9801 , 712.42664, 694.66534, 706.2037 , 698.7865 , 710.113 ],\n",
|
|||
|
|
" dtype=float32), array([711.2545 , 711.8328 , 695.2179 , 706.86926, 702.1029 , 714.1276 ],\n",
|
|||
|
|
" dtype=float32), array([715.0971 , 715.75146, 702.5245 , 714.2058 , 710.8515 , 724.1893 ],\n",
|
|||
|
|
" dtype=float32), array([714.30646, 716.84576, 702.80365, 715.27673, 710.8451 , 722.70374],\n",
|
|||
|
|
" dtype=float32), array([719.14465, 722.13525, 708.9709 , 722.64764, 717.31323, 729.5588 ],\n",
|
|||
|
|
" dtype=float32), array([716.94794, 719.9088 , 707.00244, 720.16 , 714.6256 , 728.7699 ],\n",
|
|||
|
|
" dtype=float32), array([723.4283 , 727.3508 , 714.6551 , 728.5517 , 722.06805, 736.2993 ],\n",
|
|||
|
|
" dtype=float32), array([729.0697 , 734.50745, 720.215 , 734.84576, 726.61334, 739.58093],\n",
|
|||
|
|
" dtype=float32), array([736.8218 , 742.0457 , 729.7585 , 742.5639 , 734.8822 , 747.93274],\n",
|
|||
|
|
" dtype=float32), array([737.19006, 742.66846, 730.7292 , 742.0622 , 733.61035, 748.07275],\n",
|
|||
|
|
" dtype=float32), array([742.5786 , 749.58203, 736.69275, 748.3386 , 737.7694 , 751.4996 ],\n",
|
|||
|
|
" dtype=float32), array([748.1776 , 755.5386 , 741.2965 , 752.73615, 740.20984, 753.3061 ],\n",
|
|||
|
|
" dtype=float32), array([754.22974, 760.3258 , 747.23 , 756.0053 , 743.81305, 755.99945],\n",
|
|||
|
|
" dtype=float32), array([748.5553 , 754.40576, 739.096 , 744.6117 , 730.618 , 742.1785 ],\n",
|
|||
|
|
" dtype=float32), array([751.84515, 759.0537 , 740.72003, 743.97 , 727.64844, 737.48267],\n",
|
|||
|
|
" dtype=float32), array([754.3226, 760.5805, 743.0433, 740.1709, 722.736 , 731.4394],\n",
|
|||
|
|
" dtype=float32), array([759.0515 , 765.7077 , 747.29504, 741.6952 , 724.0477 , 731.908 ],\n",
|
|||
|
|
" dtype=float32), array([758.4683 , 765.28687, 747.8231 , 738.57733, 720.3308 , 727.62244],\n",
|
|||
|
|
" dtype=float32), array([768.1985 , 775.9893 , 757.04285, 744.35986, 725.0625 , 727.0392 ],\n",
|
|||
|
|
" dtype=float32), array([769.8027 , 779.14886, 758.3488 , 742.3426 , 722.5022 , 722.41626],\n",
|
|||
|
|
" dtype=float32), array([770.56305, 782.6886 , 757.7001 , 738.8464 , 717.78284, 714.54114],\n",
|
|||
|
|
" dtype=float32), array([762.469 , 775.0614 , 748.39746, 726.78345, 706.10126, 703.67523],\n",
|
|||
|
|
" dtype=float32), array([758.5523 , 770.3217 , 743.11914, 719.5308 , 700.236 , 699.40137],\n",
|
|||
|
|
" dtype=float32), array([753.59265, 763.6109 , 735.97736, 711.8837 , 694.14264, 696.238 ],\n",
|
|||
|
|
" dtype=float32), array([757.4665 , 764.55554, 738.5043 , 715.44653, 700.5221 , 709.08716],\n",
|
|||
|
|
" dtype=float32), array([759.70984, 769.237 , 743.46326, 721.2029 , 707.24677, 715.37646],\n",
|
|||
|
|
" dtype=float32), array([763.5315 , 772.17426, 748.10803, 728.63654, 715.6052 , 724.92 ],\n",
|
|||
|
|
" dtype=float32), array([767.4251 , 777.1621 , 753.9901 , 737.1577 , 724.072 , 732.62225],\n",
|
|||
|
|
" dtype=float32), array([766.8934 , 773.8264 , 751.09375, 738.2992 , 726.15643, 737.389 ],\n",
|
|||
|
|
" dtype=float32), array([764.67377, 768.6211 , 747.7047 , 737.7364 , 725.1804 , 740.6087 ],\n",
|
|||
|
|
" dtype=float32), array([760.8852 , 763.42444, 742.20776, 735.65955, 720.21576, 735.903 ],\n",
|
|||
|
|
" dtype=float32), array([756.7726 , 757.0355 , 735.8025 , 731.2643 , 713.95856, 731.88104],\n",
|
|||
|
|
" dtype=float32), array([750.3554 , 751.9164 , 727.2225 , 723.16565, 703.58344, 721.52747],\n",
|
|||
|
|
" dtype=float32), array([740.0461, 742.6541, 716.269 , 710.8647, 690.959 , 707.6314],\n",
|
|||
|
|
" dtype=float32), array([730.5083 , 733.87695, 706.54755, 699.91705, 678.12946, 691.9137 ],\n",
|
|||
|
|
" dtype=float32), array([726.5651 , 732.63666, 705.51733, 697.27 , 674.07855, 683.60394],\n",
|
|||
|
|
" dtype=float32), array([722.51306, 730.1307 , 703.6511 , 693.8948 , 670.9812 , 679.3787 ],\n",
|
|||
|
|
" dtype=float32), array([715.70483, 723.828 , 698.10944, 687.30426, 665.7912 , 673.48505],\n",
|
|||
|
|
" dtype=float32), array([721.716 , 729.67004, 707.35657, 696.0589 , 678.0661 , 688.11914],\n",
|
|||
|
|
" dtype=float32), array([723.1698 , 730.876 , 709.45416, 699.955 , 683.57996, 695.2215 ],\n",
|
|||
|
|
" dtype=float32), array([721.6532 , 728.28143, 709.36176, 701.9152 , 687.64044, 702.2433 ],\n",
|
|||
|
|
" dtype=float32), array([729.039 , 735.14264, 718.866 , 712.88885, 700.52106, 716.4159 ],\n",
|
|||
|
|
" dtype=float32), array([739.4916 , 747.2386 , 730.4318 , 724.78186, 712.1654 , 726.9943 ],\n",
|
|||
|
|
" dtype=float32), array([720.47687, 727.87317, 704.8827 , 698.66315, 680.1756 , 694.5105 ],\n",
|
|||
|
|
" dtype=float32), array([702.9074 , 708.28876, 683.15515, 675.1121 , 654.9309 , 671.177 ],\n",
|
|||
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|||
|
|
" dtype=float32), array([1481.4567, 1484.7322, 1480.2349, 1484.6304, 1484.5311, 1485.2019],\n",
|
|||
|
|
" dtype=float32), array([1479.6699, 1485.4242, 1479.1885, 1483.961 , 1483.6782, 1482.9976],\n",
|
|||
|
|
" dtype=float32), array([1495.0712, 1504.9498, 1496.6691, 1502.781 , 1504.0496, 1499.66 ],\n",
|
|||
|
|
" dtype=float32), array([1486.9579, 1498.0474, 1489.477 , 1493.1128, 1494.5732, 1490.4225],\n",
|
|||
|
|
" dtype=float32), array([1497.3074, 1510.0963, 1504.0037, 1510.477 , 1513.3217, 1510.2777],\n",
|
|||
|
|
" dtype=float32), array([1498.2058, 1512.6973, 1508.5977, 1514.6373, 1516.3179, 1513.497 ],\n",
|
|||
|
|
" dtype=float32), array([1494.8613, 1507.9382, 1508.7716, 1512.0325, 1510.844 , 1508.8567],\n",
|
|||
|
|
" dtype=float32), array([1501.5873, 1515.6913, 1518.0746, 1520.3755, 1517.3875, 1515.4656],\n",
|
|||
|
|
" dtype=float32), array([1522.2341, 1537.6779, 1536.5604, 1537.5393, 1534.6783, 1526.4109],\n",
|
|||
|
|
" dtype=float32), array([1523.0189, 1537.0077, 1535.367 , 1532.2349, 1528.3142, 1517.5747],\n",
|
|||
|
|
" dtype=float32), array([1522.0273, 1534.7507, 1536.198 , 1529.4598, 1524.958 , 1516.2101],\n",
|
|||
|
|
" dtype=float32), array([1517.9725, 1530.0222, 1530.3217, 1521.9501, 1517.8414, 1506.5249],\n",
|
|||
|
|
" dtype=float32), array([1511.4004, 1524.0242, 1519.1508, 1508.0249, 1503.426 , 1488.5485],\n",
|
|||
|
|
" dtype=float32), array([1477.5887, 1485.7465, 1481.2251, 1466.5912, 1463.8154, 1446.1862],\n",
|
|||
|
|
" dtype=float32), array([1473.251 , 1480.0742, 1477.9437, 1461.6278, 1459.4736, 1444.7577],\n",
|
|||
|
|
" dtype=float32), array([1458.1064, 1462.689 , 1464.8788, 1445.7268, 1444.8391, 1433.1827],\n",
|
|||
|
|
" dtype=float32), array([1454.392 , 1460.7103, 1460.3464, 1441.8097, 1438.6476, 1426.1942],\n",
|
|||
|
|
" dtype=float32), array([1448.4722, 1456.7605, 1453.705 , 1435.1122, 1429.625 , 1415.939 ],\n",
|
|||
|
|
" dtype=float32), array([1425.392 , 1434.0651, 1429.7711, 1410.3441, 1404.2589, 1388.386 ],\n",
|
|||
|
|
" dtype=float32), array([1402.5212, 1411.6667, 1401.1396, 1383.8099, 1374.1315, 1353.4011],\n",
|
|||
|
|
" dtype=float32), array([1390.7423, 1399.6361, 1384.9495, 1370.3263, 1360.2627, 1336.2781],\n",
|
|||
|
|
" dtype=float32), array([1354.8442, 1361.8103, 1349.6924, 1332.3949, 1325.3546, 1300.0288],\n",
|
|||
|
|
" dtype=float32), array([1331.872 , 1335.3584, 1320.5149, 1303.7125, 1294.7102, 1267.4688],\n",
|
|||
|
|
" dtype=float32), array([1287.5886, 1289.1176, 1273.9734, 1256.2416, 1246.9854, 1221.6407],\n",
|
|||
|
|
" dtype=float32), array([1274.7943, 1273.9462, 1259.7096, 1242.058 , 1234.8624, 1212.3391],\n",
|
|||
|
|
" dtype=float32), array([1255.0616, 1253.4917, 1247.1542, 1229.5161, 1223.786 , 1206.9946],\n",
|
|||
|
|
" dtype=float32), array([1242.9927, 1242.1168, 1235.9941, 1221.6803, 1215.8387, 1198.734 ],\n",
|
|||
|
|
" dtype=float32), array([1221.1061, 1222.9034, 1215.3322, 1205.2732, 1195.7018, 1177.55 ],\n",
|
|||
|
|
" dtype=float32), array([1200.223 , 1204.9587, 1199.5072, 1190.5468, 1178.7667, 1161.4323],\n",
|
|||
|
|
" dtype=float32), array([1185.5503, 1190.9949, 1190.5211, 1181.874 , 1172.7091, 1155.4761],\n",
|
|||
|
|
" dtype=float32), array([1162.4443, 1174.5453, 1176.81 , 1167.0188, 1156.0907, 1138.903 ],\n",
|
|||
|
|
" dtype=float32), array([1146.004 , 1163.0221, 1166.7188, 1156.8767, 1139.0765, 1122.7975],\n",
|
|||
|
|
" dtype=float32), array([1143.0074, 1154.1707, 1164.512 , 1153.164 , 1144.4489, 1125.8475],\n",
|
|||
|
|
" 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), array([597.46625, 597.2494 , 598.1044 , 599.95886, 602.8901 , 606.2555 ],\n",
|
|||
|
|
" dtype=float32), array([597.83575, 598.69415, 600.55524, 603.5007 , 606.88696, 609.87384],\n",
|
|||
|
|
" dtype=float32), array([599.2894 , 601.15686, 604.11255, 607.5146 , 610.5208 , 612.9819 ],\n",
|
|||
|
|
" dtype=float32), array([601.76404, 604.72943, 608.14325, 611.1641 , 613.64276, 615.555 ],\n",
|
|||
|
|
" dtype=float32), array([605.35187, 608.7769 , 611.8085 , 614.3001 , 616.228 , 618.1106 ],\n",
|
|||
|
|
" dtype=float32), array([609.4158 , 612.45764, 614.9585 , 616.8977 , 618.7957 , 621.21204],\n",
|
|||
|
|
" dtype=float32), array([613.1119 , 615.6215 , 617.56836, 619.4777 , 621.91125, 624.82837],\n",
|
|||
|
|
" dtype=float32), array([616.2894 , 618.2435 , 620.1605 , 622.60736, 625.5434 , 628.45105],\n",
|
|||
|
|
" dtype=float32), array([618.92334, 620.84784, 623.30426, 626.25525, 629.18207, 632.41315],\n",
|
|||
|
|
" dtype=float32), array([621.5396 , 624.0053 , 626.9679 , 629.9099 , 633.1612 , 637.42206],\n",
|
|||
|
|
" dtype=float32), array([624.71075, 627.6845 , 630.63837, 633.90643, 638.19104, 642.7262 ],\n",
|
|||
|
|
" dtype=float32), array([628.40515, 631.3706 , 634.652 , 638.957 , 643.5171 , 648.47626],\n",
|
|||
|
|
" dtype=float32), array([632.10645, 635.4008 , 639.7232 , 644.3052 , 649.2911 , 654.8073 ],\n",
|
|||
|
|
" dtype=float32), array([636.1531 , 640.4923 , 645.09326, 650.10297, 655.6483 , 660.96515],\n",
|
|||
|
|
" dtype=float32), array([641.2643 , 645.8837 , 650.9147 , 656.4864 , 661.83215, 666.4663 ],\n",
|
|||
|
|
" dtype=float32), array([646.67676, 651.72833, 657.32416, 662.6966 , 667.3578 , 670.8594 ],\n",
|
|||
|
|
" dtype=float32), array([652.544 , 658.1633 , 663.56024, 668.2468 , 671.77234, 674.1133 ],\n",
|
|||
|
|
" dtype=float32), array([659.0038 , 664.42487, 669.13495, 672.6831 , 675.04456, 675.86786],\n",
|
|||
|
|
" dtype=float32), array([665.29047, 670.0237 , 673.5929 , 675.9739 , 676.81335, 676.42334],\n",
|
|||
|
|
" dtype=float32), array([670.9131 , 674.50305, 676.90247, 677.75714, 677.3796 , 676.61975],\n",
|
|||
|
|
" dtype=float32), array([675.4136 , 677.8313 , 678.7003 , 678.3346 , 677.58624, 676.83606],\n",
|
|||
|
|
" dtype=float32), array([678.7604 , 679.6439 , 679.2893 , 678.55194, 677.8134 , 677.1142 ],\n",
|
|||
|
|
" dtype=float32), array([680.5879 , 680.2448 , 679.51776, 678.7904 , 678.1031 , 678.2624 ],\n",
|
|||
|
|
" dtype=float32), array([681.2008, 680.4848, 679.768 , 679.0921, 679.2662, 680.369 ],\n",
|
|||
|
|
" dtype=float32), array([681.45245, 680.7473 , 680.08234, 680.27057, 681.3912 , 682.3875 ],\n",
|
|||
|
|
" dtype=float32), array([681.72754, 681.0746 , 681.2766 , 682.41437, 683.42786, 683.8018 ],\n",
|
|||
|
|
" dtype=float32), array([682.06805, 682.28516, 683.43976, 684.47003, 684.8587 , 684.75433],\n",
|
|||
|
|
" dtype=float32), array([683.29517, 684.468 , 685.51483, 685.91797, 685.8264 , 685.12067],\n",
|
|||
|
|
" dtype=float32), array([685.4979 , 686.5628 , 686.98047, 686.90137, 686.20605, 685.0431 ],\n",
|
|||
|
|
" dtype=float32), array([687.6127 , 688.0466 , 687.9803 , 687.2949 , 686.14014, 685.321 ],\n",
|
|||
|
|
" dtype=float32), array([689.1147 , 689.0632 , 688.38824, 687.2414 , 686.43097, 685.9478 ],\n",
|
|||
|
|
" dtype=float32), array([690.14844, 689.48615, 688.3478 , 687.5456 , 687.0715 , 686.3 ],\n",
|
|||
|
|
" dtype=float32), array([690.58685, 689.4593 , 688.6659 , 688.2003 , 687.4359 , 686.40405],\n",
|
|||
|
|
" dtype=float32), array([690.574 , 689.79193, 689.33545, 688.57776, 687.5513 , 687.3081 ],\n",
|
|||
|
|
" dtype=float32), array([690.9216 , 690.4768 , 689.7265 , 688.70483, 688.4686 , 689.3251 ],\n",
|
|||
|
|
" dtype=float32), array([691.622 , 690.8818 , 689.86554, 689.63574, 690.5018 , 692.1045 ],\n",
|
|||
|
|
" dtype=float32), array([692.0413 , 691.03357, 690.81067, 691.6856 , 693.2994 , 695.9083 ],\n",
|
|||
|
|
" dtype=float32), array([692.20593, 691.993 , 692.8774 , 694.5015 , 697.12384, 700.5293 ],\n",
|
|||
|
|
" dtype=float32), array([693.17993, 694.0768 , 695.71185, 698.3469 , 701.76746, 706.132 ],\n",
|
|||
|
|
" dtype=float32), array([695.2809 , 696.9298 , 699.57825, 703.01337, 707.3953 , 712.3286 ],\n",
|
|||
|
|
" dtype=float32), array([698.1524 , 700.8171 , 704.26746, 708.6665 , 713.6182 , 718.80695],\n",
|
|||
|
|
" dtype=float32), array([702.06036, 705.5287 , 709.9456 , 714.91583, 720.1233 , 725.1668 ],\n",
|
|||
|
|
" dtype=float32), array([706.794 , 711.2314 , 716.22095, 721.44745, 726.5087 , 731.8014 ],\n",
|
|||
|
|
" dtype=float32), array([712.5209 , 717.5324 , 722.77893, 727.8585 , 733.16925, 738.79926],\n",
|
|||
|
|
" dtype=float32), array([718.84686, 724.11597, 729.21515, 734.5449 , 740.1939 , 746.07196],\n",
|
|||
|
|
" dtype=float32), array([725.45544, 730.5767 , 735.9271 , 741.59625, 747.4939 , 753.49713],\n",
|
|||
|
|
" dtype=float32), array([731.94006, 737.31335, 743.0046 , 748.9235 , 754.94666, 761.005 ],\n",
|
|||
|
|
" dtype=float32), array([738.7007 , 744.41614, 750.3584 , 756.40375, 762.4822 , 768.18384],\n",
|
|||
|
|
" dtype=float32), array([745.8281 , 751.79584, 757.86554, 763.9668 , 769.6877 , 774.4909 ],\n",
|
|||
|
|
" dtype=float32), array([753.23285, 759.3289 , 765.4556 , 771.1987 , 776.0185 , 779.8621 ],\n",
|
|||
|
|
" dtype=float32), array([760.79126, 766.94525, 772.7135 , 777.55316, 781.4104 , 784.5472 ],\n",
|
|||
|
|
" dtype=float32), array([768.433 , 774.2283 , 779.09125, 782.9659 , 786.11395, 787.77997],\n",
|
|||
|
|
" dtype=float32), array([775.74054, 780.6286 , 784.52435, 787.6882 , 789.36084, 789.2837 ],\n",
|
|||
|
|
" dtype=float32), array([782.1629 , 786.0817 , 789.26514, 790.9492 , 790.8732 , 788.9985 ],\n",
|
|||
|
|
" dtype=float32), array([787.6354 , 790.84064, 792.5404 , 792.4703 , 790.59076, 787.5165 ],\n",
|
|||
|
|
" dtype=float32), array([792.41205, 794.12976, 794.07025, 792.1909 , 789.10767, 785.22095],\n",
|
|||
|
|
" dtype=float32), array([795.7149 , 795.66864, 793.7942 , 790.7071 , 786.8086 , 782.52637],\n",
|
|||
|
|
" dtype=float32), array([797.26263, 795.3962 , 792.3101 , 788.40454, 784.1088 , 779.78204],\n",
|
|||
|
|
" dtype=float32), array([796.9941 , 793.9122 , 790.0046 , 785.7 , 781.3588 , 777.44336],\n",
|
|||
|
|
" dtype=float32), array([795.5105 , 791.60443, 787.2959 , 782.9449 , 779.0156 , 775.7573 ],\n",
|
|||
|
|
" dtype=float32), array([793.2009 , 788.8921 , 784.53613, 780.5974 , 777.3266 , 774.42255],\n",
|
|||
|
|
" dtype=float32), array([790.4855 , 786.12836, 782.185 , 778.9059 , 775.98944, 773.3605 ],\n",
|
|||
|
|
" dtype=float32), array([787.7184 , 783.77423, 780.49146, 777.5668 , 774.9253 , 772.32886],\n",
|
|||
|
|
" dtype=float32), array([785.36176, 782.0792 , 779.1509 , 776.5008 , 773.8909 , 770.9947 ],\n",
|
|||
|
|
" dtype=float32), array([783.6659 , 780.73773, 778.08356, 775.46387, 772.5522 , 769.0831 ],\n",
|
|||
|
|
" dtype=float32), array([782.32385, 779.66956, 777.0446 , 774.12085, 770.63324, 766.92236],\n",
|
|||
|
|
" dtype=float32), array([781.2552 , 778.6291 , 775.69763, 772.19476, 768.4634 , 764.9759 ],\n",
|
|||
|
|
" dtype=float32), array([780.2136, 777.2787, 773.7648, 770.0159, 766.5074, 762.8747],\n",
|
|||
|
|
" dtype=float32), array([778.86017, 775.3396 , 771.57733, 768.0506 , 764.3952 , 760.54694],\n",
|
|||
|
|
" dtype=float32), array([776.9152 , 773.14404, 769.603 , 765.9274 , 762.0544 , 758.33655],\n",
|
|||
|
|
" dtype=float32), array([774.7119 , 771.161 , 767.46906, 763.57367, 759.83026, 756.7255 ],\n",
|
|||
|
|
" dtype=float32), array([772.72064, 769.0167 , 765.1026 , 761.33575, 758.20624, 755.50684],\n",
|
|||
|
|
" dtype=float32), array([770.5664 , 766.6378 , 762.8511 , 759.6986 , 756.97455, 754.9509 ],\n",
|
|||
|
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|
|||
|
|
" dtype=float32), array([1528.1182, 1522.9312, 1516.4202, 1508.1248, 1499.012 , 1490.2251],\n",
|
|||
|
|
" dtype=float32), array([1523.6284, 1517.0922, 1508.7592, 1499.6023, 1490.7705, 1481.5399],\n",
|
|||
|
|
" dtype=float32), array([1517.771 , 1509.4045, 1500.2045, 1491.3264, 1482.047 , 1472.9203],\n",
|
|||
|
|
" dtype=float32), array([1510.0574, 1500.8182, 1491.8947, 1482.5646, 1473.3868, 1464.6594],\n",
|
|||
|
|
" dtype=float32), array([1501.4404, 1492.4751, 1483.0946, 1473.8634, 1465.0836, 1457.0616],\n",
|
|||
|
|
" dtype=float32), array([1493.0651, 1483.6377, 1474.3528, 1465.5176, 1457.4429, 1451.9672],\n",
|
|||
|
|
" dtype=float32), array([1484.1909, 1474.8556, 1465.964 , 1457.833 , 1452.3105, 1450.9849],\n",
|
|||
|
|
" dtype=float32), array([1475.3693, 1466.4247, 1458.2357, 1452.662 , 1451.3007, 1452.068 ],\n",
|
|||
|
|
" dtype=float32), array([1466.8962, 1458.6525, 1453.0254, 1451.6239, 1452.3602, 1454.3932],\n",
|
|||
|
|
" dtype=float32), array([1459.0807, 1453.4028, 1451.9579, 1452.6583, 1454.6627, 1456.7661],\n",
|
|||
|
|
" dtype=float32), array([1453.7914, 1452.3049, 1452.9661, 1454.937 , 1457.0104, 1460.0034],\n",
|
|||
|
|
" dtype=float32), array([1452.6626, 1453.2856, 1455.219 , 1457.2579, 1460.223 , 1463.1577],\n",
|
|||
|
|
" dtype=float32), array([1453.6151, 1455.5117, 1457.5116, 1460.4438, 1463.3497, 1466.2291],\n",
|
|||
|
|
" dtype=float32), array([1455.8129, 1457.7738, 1460.669 , 1463.5411, 1466.3914, 1470.2738],\n",
|
|||
|
|
" dtype=float32), array([1458.0437, 1460.9008, 1463.7347, 1466.5511, 1470.4078, 1474.8258],\n",
|
|||
|
|
" dtype=float32), array([1461.1388, 1463.9332, 1466.7112, 1470.5375, 1474.932 , 1477.2858],\n",
|
|||
|
|
" dtype=float32), array([1464.1366, 1466.8737, 1470.6652, 1475.0317, 1477.3562, 1476.5273],\n",
|
|||
|
|
" dtype=float32), array([1467.0397, 1470.793 , 1475.1273, 1477.4186, 1476.551 , 1472.603 ],\n",
|
|||
|
|
" dtype=float32), array([1470.9229, 1475.2208, 1477.4741, 1476.5646, 1472.5695, 1465.4861],\n",
|
|||
|
|
" dtype=float32), array([1475.3148, 1477.5261, 1476.5697, 1472.5236, 1465.3844, 1455.3921],\n",
|
|||
|
|
" dtype=float32), array([1477.5768, 1476.5692, 1472.4677, 1465.2689, 1455.2128, 1444.9747],\n",
|
|||
|
|
" dtype=float32), array([1476.5667, 1472.405 , 1465.142 , 1455.0183, 1444.7174, 1434.4945],\n",
|
|||
|
|
" dtype=float32), array([1472.3394, 1465.0073, 1454.8112, 1444.4432, 1434.1597, 1424.2051],\n",
|
|||
|
|
" dtype=float32), array([1464.8693, 1454.5956, 1444.1555, 1433.8064, 1423.7938, 1412.4114],\n",
|
|||
|
|
" dtype=float32), array([1454.3768, 1443.8593, 1433.4387, 1423.363 , 1411.9199, 1397.0203],\n",
|
|||
|
|
" dtype=float32), array([1443.5598, 1433.0625, 1422.9174, 1411.4081, 1396.438 , 1378.7422],\n",
|
|||
|
|
" dtype=float32), array([1432.6832, 1422.4631, 1410.8806, 1395.8341, 1378.0607, 1358.4681],\n",
|
|||
|
|
" dtype=float32), array([1422.0061, 1410.3448, 1395.2147, 1377.3568, 1357.682 , 1334.1814],\n",
|
|||
|
|
" dtype=float32), array([1409.807 , 1394.5878, 1376.6378, 1356.8728, 1333.2787, 1306.7471],\n",
|
|||
|
|
" dtype=float32), array([1393.9598, 1375.9122, 1356.0491, 1332.3524, 1305.7184, 1277.7213],\n",
|
|||
|
|
" dtype=float32), array([1375.1869, 1355.2205, 1331.4124, 1304.6658, 1276.5626, 1251.4374],\n",
|
|||
|
|
" dtype=float32), array([1354.3934, 1330.4692, 1303.6006, 1275.3795, 1250.1583, 1228.0193],\n",
|
|||
|
|
" dtype=float32), array([1329.5302, 1302.5347, 1274.1849, 1248.8547, 1226.6302, 1206.6265],\n",
|
|||
|
|
" dtype=float32), array([1301.4751, 1272.9924, 1247.5408, 1225.2161, 1205.1346, 1186.5587],\n",
|
|||
|
|
" dtype=float32), array([1271.8085, 1246.2311, 1223.7928, 1203.6174, 1184.9695, 1167.9548],\n",
|
|||
|
|
" dtype=float32), array([1244.9325, 1222.376 , 1202.0918, 1183.354 , 1166.2733, 1151.0895],\n",
|
|||
|
|
" dtype=float32), array([1220.9723, 1200.5748, 1181.7313, 1164.5656, 1149.3224, 1135.3484],\n",
|
|||
|
|
" dtype=float32), array([1199.0729, 1180.1188, 1162.8508, 1147.5283, 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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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 3600x2400 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"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
|
|||
|
|
}
|