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update
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2
train.py
2
train.py
@@ -108,7 +108,7 @@ def experiment(repeats, series, seed):
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t2 = test.shape[0] % batch_size
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train_trimmed = train_scaled[t1:, :]
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lstm_model = fit_lstm(train_trimmed, batch_size, 300, 4)
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lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4)
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# forecast the entire training dataset to build up state for forecasting
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print(train_trimmed)
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print(train_trimmed[:, 0])
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