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Changelog.md

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RELEASE 5.0 (released on 2023-07-14)

  1. Python 3.11 Support : Python 3.11 support #227
  2. New Hardware Support : RISC-V Hardware Platform Validation #208
  3. New Performance Measures : Outlier-resistant forecasting Performance Measures #209, Add Differentiable Variant of SMAPE Performance Measure #221
  4. Model Selection Improvement : Investigate Model Esthetics for PyAF #212, Investigate Large Horizon Models #213 , Revisit Model Complexity Definition #223, Use MASE by default for PyAF Model Selection #229
  5. Signal Transformation Improvements : Use MaxAbsScaler for some Multiplicative Signal Transformations #235, Pyaf 5.0 Final Touch 8 : Use an Optimal Choice Rule for the Quantization Signal transform #239
  6. Generic Modeling : PyAF 5.0 Final Touch 1 : discard some non-significant components #230, PyAF 5.0 Final Touch 2: Disable alpha in ridge regressions #231, Pyaf 5.0 Final Touch 5 : Add more info about Exogenous Data Used in ARX Models #236, Pyaf 5.0 Final Touch 7 : Improve the Guess of Window Length for Moving Average Trends #238
  7. Plotting Functions Improvements and Bug Fixes : Bad plot for shaded area around prediction intervals in hourly data #216, Forecast Quantiles Plots Improved #225, Pyaf 5.0 Final Touch 3 : report plot filenames in the logs #232
  8. New Docs : Provide some UML docs for PyAF integrators #233
  9. Bug Fixes : Failure to build a multiplicative ozone model with Lag1 trend #220
  10. PyAF "Forecast Tasks" : Use PyTorch as the reference deep learning framework/architecture for future projects #211, Automate Prototyping Activities - R-based Models #217
  11. Recurrent Tasks : Re-run the Benchmarking process for PyAF 5.0 #222, Run some Sanity Checks for PyAF 5.0 #224, Pyaf 5.0 Final Touch 4 : Add More Tests #234, Pyaf 5.0 Final Touch 6 : Disable Timing Loggers by default #237

RELEASE 4.0 (released on 2022-07-14)

  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

RELEASE 3.0 (released on 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