https://github.com/antoinecarme/pyaf
Revision 09efc13fef8835d1aab3e3cb73c22ee35ed37cd9 authored by Antoine Carme on 17 April 2022, 00:22:32 UTC, committed by Antoine Carme on 17 April 2022, 00:22:32 UTC
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Tip revision: 09efc13fef8835d1aab3e3cb73c22ee35ed37cd9 authored by Antoine Carme on 17 April 2022, 00:22:32 UTC
Make the Modeling Scale when the Number of Exogenous Variables Increases #198. Added an exog preselection system. WIP.
Tip revision: 09efc13
Changelog.md

RELEASE 3.0 (expected 2021-07-14)

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)

RELEASE 2.0 (released on 2020-07-14)

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

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