Time Series Augmentation in pytorch forecasting


I have count time series of demand data and some covariates like weather information every hour. I have used 168 hours (7 days) for encoder and 24 hours (next day) for decoder in DeepAR pytorch forecasting. E.G. using MTWTFSS for encoder to predict M (Monday)

After doing much testing I find that the 24 hours in prediction is more correlated with NOT the previous 7 days. It is more correlated with the same days in the past week. So I would need to use

MMMMMMM (mondays of previous weeks) to predict M (next monday).

Is it possible to tell TimeSeriesDataset object to train using this type of inputs?

I cannot manually create this like below


because it will take any subsequence inside this time series like MMMMTTTT to use for encoder (MMMMTTT) and decoder (T). I do not want this. so is there a way to tell TimeSeriesDataset object to only sample the time series sequentially from the beginining without any overlaps during training? So that I can just feed the input time series as