python深度学习之基于LSTM时间序列的股票价格预测

LSTM时间序列预测 python代码——import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf tf.reset_default_graph() #——————————————————导入数据—————————————————————— #读入数据 data = pd.read_excel(r'C:\Users\10025\Desktop\完整数据.xls') data = data.values #定义常量 rnn_unit=10 #hidden layer units input_size=3 #数据输入维度 output_size=1 #数据输出入维度 lr=0.0006 #学习率 #获取训练集 def get_train_data(batch_size=60,time_step=20,train_begin=0,train_end=5800):#用前5800个数据作为训练样本 batch_index=[] data_train=data[train_begin:train_end] normalized_train_data=(data_train-np.mean(data_train,axis=0))/np.std(data_train,axis=0) #标准化 train_x,train_y=[],[] #训练集 for i in range(len(normalized_train_data)-time_step): if i % batch_size==0: