时间序列预测—双向LSTM(Bi-LSTM)

        本文展示了使用双向LSTM(Bi-LSTM)进行时间序列预测的全过程，包含详细的注释。整个过程主要包括：数据导入、数据清洗、结构转化、建立Bi-LSTM模型、训练模型（包括动态调整学习率和earlystopping的设置）、预测、结果展示、误差评估等完整的时间序列预测流程。
  本文使用的数据集在本人上传的资源中，链接为mock_kaggle.csv

代码如下：

import pandas as pd
import numpy as np
import math
import keras
from matplotlib import pyplot as plt
from matplotlib.pylab import mpl
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
from keras import backend as K
from keras.layers import LeakyReLU
from sklearn.metrics import mean_squared_error # 均方误差
from keras.callbacks import LearningRateScheduler
from keras.callbacks import EarlyStopping
from tensorflow.keras import Input, Model,Sequential
from keras.layers import Bidirectional#, Concatenate

mpl.rcParams['font.sans-serif'] = ['SimHei']   #显示中文
mpl.rcParams['axes.unicode_minus']=False       #显示负号

取数据

data=pd.read_csv('mock_kaggle.csv',encoding ='gbk',parse_dates=['datetime'])
Date=pd.to_datetime(data.datetime)
data['date'] = Date.map(lambda x: x.strftime('%Y-%m-%d'))
datanew=data.set_index(Date)
series = pd.Series(datanew['股票'].values, index=datanew['date'])

series

date
2014-01-01    4972
2014-01-02    4902
2014-01-03    4843
2014-01-04    4750
2014-01-05    4654
              ... 
2016-07-27    3179
2016-07-28    3071
2016-07-29    4095
2016-07-30    3825
2016-07-31    3642
Length: 937, dtype: int64

滞后扩充数据

dataframe1 = pd.DataFrame()
num_hour = 16
for i in range(num_hour,0,-1):
    dataframe1['t-'+str(i)] = series.shift(i)
dataframe1['t'] = series.values
dataframe3=dataframe1.dropna()
dataframe3.index=range(len(dataframe3))

dataframe3

显示详细信息

921 rows × 17 columns

二折划分数据并标准化

pd.DataFrame(np.random.shuffle(dataframe3.values))  #shuffle
pot=len(dataframe3)-12
train=dataframe3