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update
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2
train.py
2
train.py
@@ -102,7 +102,7 @@ scaler, train_scaled, test_scaled = scale(train, test)
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lstm_model = fit_lstm(train_scaled, 32, 30, 4)
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# forecast the entire training dataset to build up state for forecasting
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train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
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lstm_model.predict(train_reshaped, batch_size=1)
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lstm_model.predict(train_reshaped, batch_size=32)
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# walk-forward validation on the test data
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predictions = list()
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