Hi @Gautham_Kumaran,
Thanks a lot for your work. I am using this as reference for one of my projects. However I have a query regarding the data we pass on to the encoder.
So imagine a case in which we have 500 series for predictions. my input seq length is 180 and output seq length is 150. So, the input data shape will be something like ( batch_size, 180, 500) right? and the output seq_length will be ( batch_size, 150, 500). (please correct me if I’m wrong here)
Now imagine a case where we have some external factors (F1, F2, F3) that effect the series and we have 1 year of data. I was thinking of reshaping the data to (365, 7, 500) where:
365: no of batches (each batch represents a day of each time series)
7: (F1, F2, F3, day, month, year, product_id)
500: no of time series
output shape: if you’re gonna predict for 1 day: (1, 1, 500),
where the second dimension is the sales with respect to those external factors(F1, F2, F3).
In this case, if we use an attention model, all external factors will be assigned proper weights in order to predict the sales data right? Do you think this would be a good approach? Please help me out.