Summary

Our journey with deep learning for time series has finally reached a conclusion with us reviewing a few specialized architectures for time series forecasting. We got an understanding of why it makes sense to have specialized architectures for time series and forecasting and went on to understand how different models such as N-BEATS, N-BEATSx, N-HiTS, Autoformer, TFT, PatchTST, TiDE, and TSMixer work. In addition to covering the architecture and theory behind it, we also looked at how we can use these models on real datasets using neuralforecast by NIXTLA. We never know which model will work better with our dataset, but as practitioners, we need to develop intuition that will guide us in the experimentation. Knowing how these models work behind the scenes is essential in developing that intuition and will help us experiment more efficiently.

This brings this part of this book to a close. At this point, you should be much more comfortable with using DL for time series forecasting...