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 now understand how different models such as N-BEATS, N-BEATSx, N-HiTS, Informer, Autoformer, and TFT work.
We also looked at how we can apply those models using PyTorch Forecasting. For the models such as Informer and Autoformer that were not implemented in PyTorch Forecasting, we saw how we can port normal PyTorch models into a form that can be used with PyTorch Forecasting. Models such as N-BEATS and TFT also offer interpretability and we explored those use cases as well.
To top this off, we covered probabilistic forecasting at a high level and provided references so that you can start your journey of looking at them. 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 problems.
In the next part of this book, we will...
