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Newnius
2020-05-02 08:50:39 +08:00
parent e60dc18eb8
commit b8e6dfcbc2
2 changed files with 35 additions and 36 deletions
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10
train.py
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@@ -111,18 +111,18 @@ lstm_model.predict(train_reshaped, batch_size=1)
# walk-forward validation on the test data
predictions = list()
for i in range(len(test_scaled)):
for j in range(len(test_scaled)):
# make one-step forecast
X, y = test_scaled[i, 0:-1], test_scaled[i, -1]
X, y = test_scaled[j, 0:-1], test_scaled[j, -1]
yhat = forecast_lstm(lstm_model, 1, X)
# invert scaling
yhat = invert_scale(scaler, X, yhat)
# invert differencing
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - j)
# store forecast
predictions.append(yhat)
expected = raw_values[len(train) + i + 1]
print('Month=%d, Predicted=%f, Expected=%f' % (i + 1, yhat, expected))
expected = raw_values[len(train) + j + 1]
print('Month=%d, Predicted=%f, Expected=%f' % (j + 1, yhat, expected))
# report performance
rmse = sqrt(mean_squared_error(raw_values[-12:], predictions))