Get best parameters for time series without losing data
How you should split the data depends on some factors:
If you have a seasonal influence over a year, you can take a complete year for validation and two years for training.
If your data can be predicted from the last n-weeks, you can take some random n-week splits from the full dataset.
Whats more important here is that I think there's an error in your training pipeline. You should have a sliding window over n-weeks over the full training data and always predict the next 8 weeks from every n-week sequence.
I'm trying to do demand sensing for a dataset. Presently I have 157 weeks of data(~3years) and I have to predict next month(8 weeks).In the training dataset, I'm using 149weeks as a train and the last 8 weeks as Val to get the best hyperparameters. But I have observed that in the pred result, there's a huge gap in wmapes between Val and pred. I'm not sure if im overfitting because Val wmape is good.
the aim is to get best parameters such that the result pred will good for last month(last 4 weeks/8weeks).
note: there is a gap in train and pred i.e. if the train is till 31st jan22, pred will start from 1st mar22.
How can I overcome this problem?
Details: dataset: timeseries , algo: TCNmodel(darts lib),lang:python.