diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 42c1a53..38fa7ac 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -3,7 +3,6 @@
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diff --git a/train.py b/train.py
index b09f6b8..9f63cf8 100644
--- a/train.py
+++ b/train.py
@@ -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))