googlebot:

You can transform last hidden state and make it initial state for another forecasting RNN. You may feed it some future time measure, last output (if you do one step at a time) or even nothing at all. That’s decoder too.


I tried encoder-decoder model without attention like you have described and by passing my previous forecast as the input to the next decoder cell, but this didn’t give me an improvement over my previous model of feeding the results from all the encoder GRU cells to a Linear layer.
And I’m seeing an issue with passing the lags directly to the decoder too. The attention model is currently able to look into a window of values around the periodically important lag. For example, If I choose to pass t - 24 lag to my decoder, attention model does this better by giving high scores to t - 25, t - 24 and t - 26, with the peak at t - 24.
I can understand your point that using attention is resource-intensive, but provided I want to squeeze out the extra performance from the model, is this implementation of attention alright? Is there any other example of using attention for time-series forecasting?