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Newnius 2020-05-02 09:33:14 +08:00
parent d500ec5877
commit 868a231f42
2 changed files with 14 additions and 12 deletions
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18
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8
train.py
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@ -81,6 +81,8 @@ def forecast_lstm(model, batch_size, X):
return yhat[0, 0]
batch_size = 12
# load dataset
series = read_csv('data.csv', header=0, index_col=0, squeeze=True)
@ -99,17 +101,17 @@ train, test = supervised_values[0:-12], supervised_values[-12:]
scaler, train_scaled, test_scaled = scale(train, test)
# fit the model
lstm_model = fit_lstm(train_scaled, 32, 30, 4)
lstm_model = fit_lstm(train_scaled, batch_size, 30, 4)
# forecast the entire training dataset to build up state for forecasting
train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
lstm_model.predict(train_reshaped, batch_size=32)
lstm_model.predict(train_reshaped, batch_size=batch_size)
# walk-forward validation on the test data
predictions = list()
for j in range(len(test_scaled)):
# make one-step forecast
X, y = test_scaled[j, 0:-1], test_scaled[j, -1]
yhat = forecast_lstm(lstm_model, 32, X)
yhat = forecast_lstm(lstm_model, batch_size, X)
# invert scaling
yhat = invert_scale(scaler, X, yhat)
# invert differencing