0.5.0 (2023-04-03)

Python 3.10 support.

New features and methods
- Improvements on modeling holidays
  
  @Yi Su: Added HolidayGrouper. Holidays with a similar effect are grouped together to have fewer and more robust coefficients. With this feature, holidays are better modeled with improved forecast accuracy. See instructions in Auto Configuration Tools.
  
  @Kaixu Yang: Added support for holiday neighboring impact for data with frequency longer than daily through the daily_event_neighbor_impact parameter (e.g. this enables modeling holidays on weekly data where the event dates may not fall on the exact timestamps); added holiday neighboring events (i.e. the lags of an actual holiday can be specified in the model) through the daily_event_shifted_effect parameter. See details at Holidays and Events.
  
  @Yi Su: Added holiday indicators. Now users can specify “is_event_exact”, “is_event_adjacent”, “is_event” (a union of both) as extra_pred_cols in the model. See details at Holidays and Events.
- @Reza Hosseini: Added DST indicators. Now users can specify “us_dst” or “eu_dst” in extra_pred_cols. You may also use get_us_dst_start/end, get_eu_dst_start/end functions in timeseries_features to get the dates.
- @Yi Su: Theoretical improvements for the volatility model in linear and ridge algorithm for more accurate variance estimate and prediction intervals.
- @Phil Gaudreau: Added new evaluation metric: mean_interval_score.
- @Brian Vegetabile: Enhanced components plot that consolidates previous forecast breakdown functionality. The redesign provides a cleaner visual and allows for flexible breakdowns via regular expressions. See examples in Simple Forecast.
Library enhancements
- @Kaixu Yang: Python 3.10 support. Deprecated support for lower Python versions.
- @Sayan Patra: New utility function: get_exploratory_plots to easily generate exploratory data analysis (EDA) plots in HTML.
- @Kaixu Yang: Added optimize_mape option to quantile regression. It uses 1 over y as weights in the loss function. See QuantileRegression for details.
Bug fixes
- @Qiang Fei: In case of simulation, now min_adimissible_value and max_adimissible_value will correctly cap the simulated values. Additionally, errors are propagated through simulation steps to make the intervals more accurate.
- @Yi Su, @Sayan Patra: Now train_end_date is always respected if specified by the user. Previously it got ignored if there are trailing NA’s in training data or anomaly_df imputes the anomalous points to NA. Also, now train_end_date accepts a string value.
- @Yi Su: The seasonality order now takes None without raising an error. It will be treated the same as False or zero.

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.