基于MLP-CNN-LSTM-CNN-LSTM时间序列预测-编码器-解码器LSTM多步预测Keras-python源码.zip

# univariate cnn-lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.layers import Flatten from keras.layers import TimeDistributed from keras.layers.convolutional import Conv1D from keras.layers.convolutional import MaxPooling1D # define dataset X = array([[10, 20, 30, 40], [20, 30, 40, 50], [30, 40, 50, 60], [40, 50, 60, 70]]) y = array([50, 60, 70, 80]) # reshape from [samples, timesteps] into [samples, subsequences, timesteps, features] X = X.reshape((X.shape[0], 2, 2, 1)) # define model model = Sequential() model.add(TimeDistributed(Conv1D(filters=64, kernel_size=1, activation='relu'), input_shape=(None, 2, 1))) model.add(TimeDistributed(MaxPooling1D(pool_size=2))) model.add(TimeDistributed(Flatten())) model.add(LSTM(50, activation='relu')) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # fit model model.fit(X, y, epochs=500, verbose=0) # demonstrate prediction x_input = array([50, 60, 70, 80]) x_input = x_input.reshape((1, 2, 2, 1)) yhat = model.predict(x_input, verbose=0) print(yhat)