Pyaf

(all coming fixes will go in the 5.0-FIXES branch)

1. Python 3.11 support
2. Better support for long temr models.
3. Improved Model Selection Procedure.
4. Improved Model Complexity Definition 223
5. Improved Plots (titles + model formula/details)
6. Improved Quantile Plots color maps 225
7. New Platforms : RISC-V Hardware Platform Validation 208
8. New Perf Measures : Outlier-resistant forecasting Performance Measures 209 + Add Differentiable Variant of SMAPE Performance Measure 221
9. Use PyTorch as the reference deep learning framework/architecture for future projects 211
10. Experimentations : Investigate Model Esthetics for PyAF 212, Automate Prototyping Activities - R-based Models 217
11. Bugs fixed : Failure to build a multiplicative ozone model with Lag1 trend 220, Bad plot for shaded area around prediction intervals in hourly data 216
12. Run some Sanity Checks for PyAF 5.0 224
13. Use MASE by default for PyAF Model Selection 229

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1. Python 3.10 support 186
2. Add Multiplicative Models/Seasonals 178
3. Speed Performance Improvements : 190 , 191
4. Exogenous data support improvements : 193, 197, 198
5. PyAF support for ARM64 Architecture 187
6. PyTorch support : 199
7. Improved Logging : 185
8. Bug Fixes : 156, 179, 182, 184
9. Release Process : Pre-release Benchmarks 194
10. Release Process : Profiling and Warning Hunts 195
11. Release Process : Review Existing Docs 196, 35

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1. Python 3.9 support 149
2. Probabilistic Forecasting : Forecast quantiles (140), CRPS (74), Plots and Docs (158).
3. Add LightGBM based models 143
4. Add more Performance Measures : MedAE (144) , LnQ ( 43 )
5. PyAF Powerpc support (IBM S822xx) 160
6. More Parallelization Efforts (145)
7. Add Missing Data Imputation Methods (146 )
8. Improved long signals modeling (167)
9. Warning Hunts (153)
10. Some Bug Fixes (163, 142, 168).
11. Switched to Circle-CI (164)
12. Plot Functions Improvement 169
13. Model Complexity Improvement (171)
14. Documentation review/corrections (174)

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1. Time column is normalized frequently leading to a performance issue. Profiling. Significant speedup. Issue 121
2. Corrected PyPi packaging. Issue 123
3. Allow using exogenous data in hierarchical forecasting models. Issue 124
4. Properly handle very large signals. Add Sampling. Issue 126
5. Add temporal hierarchical forecasting. Issue 127
6. Analyze Business Seasonals (HourOfWeek and derivatives) . Issue 131
7. Improved logs (More model details). Issue 133, 134, 135
8. More robust scycles (use target median instead of target mean encoding). Issue 132
9. Analyze Business Seasonals (WeekOfMonth and derivatives). Issue 137
10. Improved JSON output (added Model Options). Issue 136
11. Improved cpu usage (parallelization) for hierarchical models. Issue 115
12. Speedups in multiple places : forecasts generation, plotting, AR Modelling (feature selection).
13. Last minute fixes