I was trying to use Keras' TimeSeriesGenerator function. As stated in Keras' document, my data and target lengths are equal. I defined the length input as 200.

Using, **fit_generator** and **predict_generator**, I fitted the data and predict the outcome. The problem starts here. When I tried to calculate **MAE** with values the prediction and real data, **mae** function of **scikit** gave me an error for lengths of the inputs.

The **predicted data** is less than the **y_test data** by the amount of lookback. (.ie. `len(predicted_values) = 1000, len(y_test) = 1200`

).

Therefore, I can not calculate the mean absolute error. Is there a way to change how Keras handle this case ? I assume, the algorithm just ignore the first 200 rows.

*(Data is scaled by MinMaxScaler. So, I have to inverse transform the data in order to calculate the real MAE value instead of the scaled version. That's why I am not using the evaulate_generator for MAE)*