Changelog

0.3.0 (2021-12-14)

  • New tutorials * @Reza Hosseini: Monthly time series forecast. * @Yi Su: Weekly time series forecast. * @Albert Chen: Forecast reconciliation. * @Kaixu Yang: Forecast one-by-one method.

  • New methods * @Yi Su: Lagged regressor (method was released in 0.2.0 but documentation was added in this release). * @Kaixu Yang @Saad Eddin Al Orjany: Model storage (method was released in 0.2.0 but documentation was added in this release). * @Kaixu Yang: Silverkite Multistage method for fast training on small granularity data (with tutorial). * @Albert Chen: Forecast reconciliation with interface and defaults optimized.

  • New model templates * @Yi Su: SILVERKITE_WITH_AR: The SILVERKITE template with autoregression. * @Yi Su: SILVERKITE_DAILY_1: A SimpleSilverkite template designed for daily data with forecast horizon 1. * @Kaixu Yang: SILVERKITE_TWO_STAGE: A two stage model using the Silverkite Multistage method that is good for sub-daily data with a long history. * @Kaixu Yang: SILVERKITE_MULTISTAGE_EMPTY: A base template for the Silverkite Multistage method.

  • Library enhancements and bug fixes * @Yi Su: Updated plotly to v5. * @Reza Hosseini: Use explicit_pred_cols, drop_pred_cols to directly specify or exclude model formula terms (see Custom Parameters). * @Reza Hosseini: Use simulation_num to specify number of simulations to use for generating forecasts and prediction intervals. Applies only if any of the lags in autoreg_dict are smaller than forecast_horizon (see Auto-regression). * @Reza Hosseini: Use normalize_method to normalize the design matrix (see Custom Parameters). * @Yi Su: Allow no CV and no backtest in pipeline. * @Albert Chen: Added synthetic hierarchical dataset. * Bug fix: cv_use_most_recent_splits in EvaluationPeriodParam was previously ignored. * @Albert Chen @Kaixu Yang @Reza Hosseini @Saad Eddin Al Orjany @Sayan Patra @Yi Su: Other library enhancements and bug fixes.

0.2.0 (2021-06-30)

  • @Kaixu Yang: Removed the dependency on fbprophet and change it to optional.

  • @Kaixu Yang @Saad Eddin Al Orjany: Added model dumping and loading for storing (see Forecaster.dump_forecast_result and Forecaster.load_forecast_result).

  • @Kaixu Yang @Reza Hosseini: Added forecast one-by-one method.

  • @Sayan Patra: Added the support of AutoArima by pmdarima, see the AUTO_ARIMA template.

0.1.1 (2021-05-12)

  • First release on PyPI.