Changelog

0.4.0 (2022-07-15)

  • New features and methods

    • @Reza Hosseini: Forecast interpretability. Forecasts can now be broken down to grouped components: trend, seasonality, events, autoregression, regressors, intercept, etc.

    • @Sayan Patra: Enhanced components plot. Now supports autoregression, lagged regressors, residuals; adds support for centering.

    • @Kaixu Yang: Auto model components. (1) seasonality inferrer (2) holiday inferrer (3) automatic growth.

    • @Kaixu Yang: Lag-based estimator. Supports lag-based forecasts such as week-over-week.

    • @Reza Hosseini: Fast simulation option. Provides a better accuracy and speed for mean prediction when simulation is used in autoregression.

    • @Kaixu Yang: Quantile regression option for Silverkite fit_algorithm.

  • New model templates

    • @Kaixu Yang: AUTO. Automatically chooses templates based on the data frequency, forecast horizon and evaluation configs.

    • @Reza Hosseini, @Kaixu Yang: SILVERKITE_MONTHLY - a SimpleSilverkite template designed for monthly time series.

    • @Kaixu Yang: SILVERKITE_WOW. Uses Silverkite to model seasonality, growth and holiday effects, and then uses week-over-week to fit the residuals. The final prediction is the total of the two models.

  • New datasets

    • 4 hourly datasets: Solar Power, Wind Power, Electricity, San Francisco Bay Area Traffic.

    • 1 daily dataset: Bitcoin Transactions.

    • 2 monthly datasets: Sunspot, FRED House Supply.

  • Library enhancements and bug fixes

    • The SILVERKITE template has been updated to include automatic autoregression and changepoint detection.

    • Renamed SilverkiteMultistageEstimator to MultistageForecastEstimator.

    • Renamed the normalization method “min_max” to “zero_to_one”.

    • @Reza Hosseini: Added normalization methods: “minus_half_to_half”, “zero_at_origin”.

    • @Albert Chen: Updated tutorials.

    • @Yi Su: Upgraded fbprophet 0.5 to prophet 1.0.

    • @Yi Su: Upgraded holidays to 0.13

    • @Albert Chen @Kaixu Yang @Yi Su: Speed optimization for Silverkite.

    • @Albert Chen @Reza Hosseini @Kaixu Yang @Sayan Patra @Yi Su: Other library enhancements and bug fixes.

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.