- Noteboook updated from this source article, with a famous dataset
- What was added :
- clarifies steps / order making data stationary / normalization (a common mistake/lack of in a lot of online ressources)
- Make data stationary (first order diff). Not mandatory with a lstm but very common, recommended and usually improves performance
- More structure / pipelined : transform functions (normalize / inverse diff via cumsum etc.), steps / notes on arrays shape etc.
- Normalization (minmax)
- Input size (lookback) directly set to 4 (vs. 1, then 4)
- benchmark/viz models accuracy with and without statio. + scaling
Run:
Personally, running it in a dockerized, gpu/cuda-enabled pytorch/notebook environment (cf. github/gpu-jupyter)