I'm currently reading the paper Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting: https://arxiv.org/abs/1912.09363v3
However, I had to stop at page 7/8, which contains the different pieces of the TFT, because I lack the knowledge of most of the pieces mentioned there. Thus, my following questions are:
1.) What are the different components I have to understand, in order to understand how the TFT works? So far I got: LSTM (and Gates), Transformers (with Encoders/Decoders), and Multi-Headed-Attention-Blocks, are there more parts I have to understand the TFT, e.g. what about the GRN or Dense?
2.) Do you know of any literature that explains these components in terms of time-series AND are not that math-heavy? I normally learn the maths from an example much better. So far, I read through a few blogs, looked some tutorials or vids, even a video about the paper itself, but they do not focus so strong on the individual components. Furthermore, nearly every tutorial explains parts of the TFT as a problem of speech-translation for example, and not as a time-series problem.
Although I did some transfer to time-series. I have no further literature to crossvalidate my thoughts to.
So far I read/viewed:
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) by Brandon Rohrer:https://www.youtube.com/watch?v=WCUNPb-5EYI
- Short intro to RNN and then LSTM: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
- Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting by Arvid Kingl https://www.youtube.com/watch?v=M7O4VqRf8s4&t=1106s
- Transformer Neural Networks by CodeEmporium: https://www.youtube.com/watch?v=TQQlZhbC5ps
All the best