Collect Ideas for the PyAF 5.0 roadmap

[antoinecarme]

This is an issue to serve as a honeypot for all ideas that come and can be included in the next PyAF release (5.0, expected on 2023-07-14).

First ideas :

1. Forecasting Models based on Deep Learning Attention Mechanisms
2. Recurrent tasks : More hardware architectures, forecasting performance measures, benchmarks, bug fixes, docs and optimization/profiling.
3. User feedback and issues

Ideally, the main part of the implementation will take place during the coming covid lockdown (Fall 2022 ;-).

[antoinecarme]

Large Horizon Models (H large enough). Profiling for CPU/memory/speed.

https://github.com/antoinecarme/pyaf/issues/213

[antoinecarme]

Model Esthetics

https://github.com/antoinecarme/pyaf/issues/212

[antoinecarme]

Get rid of Keras and tensorflow. Use PyTorch. Less is better. Minimal is beautiful.

https://github.com/antoinecarme/pyaf/issues/211

[antoinecarme]

PyAF will use PyTorch as its deep learning architecture for future projects.

https://github.com/antoinecarme/pyaf/issues/211

[antoinecarme]

Investigate business days / hours impact.

https://github.com/antoinecarme/pyaf/issues/210

[antoinecarme]

Add some outlier-resistant forecasting performance measures for robustness.

https://github.com/antoinecarme/pyaf/issues/209

[antoinecarme]

New Hardware Architecture : RISC-V

https://github.com/antoinecarme/pyaf/issues/208

[antoinecarme]

Investigate Threshold AR (TAR) Models

https://github.com/antoinecarme/pyaf/issues/214

[antoinecarme]

Investigate TSMARS Models

#215

[antoinecarme]

#221

[antoinecarme]

Implemented/Processed So far :

2. #208
3. #209
4. #211
5. #212
6. #213
7. #216
8. #217
9. #220
10. #221
11. #223
12. #224
13. #225

[antoinecarme]

Release Process starts.

#228

CLOSING