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update
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6
train.py
6
train.py
@@ -110,10 +110,10 @@ lstm_model.predict(train_reshaped, batch_size=batch_size)
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predictions = list()
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for j in range(len(test_scaled)):
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# make one-step forecast
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X, y = test_scaled[j, 0:-1], test_scaled[j, -1]
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yhat = forecast_lstm(lstm_model, batch_size, X)
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#X, y = test_scaled[j, 0:-1], test_scaled[j, -1]
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yhat = forecast_lstm(lstm_model, batch_size, test_scaled)
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# invert scaling
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yhat = invert_scale(scaler, X, yhat)
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yhat = invert_scale(scaler, test_scaled, yhat)
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# invert differencing
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yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - j)
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# store forecast
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