LSTM-时间序列预测.zip

# 代码功能: LS-TM 循环神经网络，预测时间序列------------------------------------ # 第1步: 处理原始数据集, 归一化，制作X_train、Y_train、X_test、Y_test # 第2步: 训练LS-TM网络, epoch=300 # 第3步: 时间序列预测 import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import LSTM, Dense, Activation # read raw data csv = pd.read_csv('international-airline-passengers.csv', usecols=['passengers']) min_max = MinMaxScaler() # normalize raw_data = min_max.fit_transform(csv) # 144 # make training set、testing set seq_len = 10 data = [] for i in range(len(raw_data)-seq_len): data.append(raw_data[i:i+seq_len+1]) data = np.array(data) # (134, 11, 1) X_train = data[0:120, :-1] # (120, 10, 1) Y_train = data[0:120, -1] # (120, 1) X_test = data[120:len(data), :-1] # (14, 10, 1) Y_test = data[120:len(data), -1] # (14, 1) # RNN structure model = Sequential() model.add(LSTM(units=256, input_shape=(10, 1), return_sequences=True)) model.add(LSTM(units=256)) model.add(Dense(units=1)) model.add(Activation('linear')) model.compile(loss='mse', optimizer='adam') model.fit(X_train, Y_train, batch_size=20, epochs=300) # predict train time series plt.plot(min_max.inverse_transform(Y_train), 'b', label='Train real data') plt.plot(min_max.inverse_transform(model.predict(X_train)), 'r:', label='LS_TM predict') plt.legend() plt.savefig('1.jpg') plt.show() # predict test time series plt.plot(min_max.inverse_transform(Y_test), 'b--', marker='o', label='Test real data') plt.plot(min_max.inverse_transform(model.predict(X_test)), 'r:', marker='*', label='LS_TM predict') plt.legend() plt.savefig('2.jpg') plt.show()