Greykite

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* Greykite AD (Anomaly Detection) is now available.
It improves upon the out-of-box confidence intervals generated by Silverkite, by automatically tuning the confidence intervals
and other filters (e.g. based on ``Absolute Percentage Error (APE)``) using expected alert rate information and/ or anomaly labels, if available.
It allows the users to define robust objective function, constraints and parameter space to optimize the confidence intervals.
For example user can target a minimal recall level of 80% while maximizing precision. Additionally, the users can specify a
minimum error level to filter out anomalies that are not business relevant. The motivation to include criteria other than
statistical significance is to bake in material/ business impact into the detection.

* Reza Hosseini: Devised the core anomaly detection library structure. Added base ``Detector`` module.
* Reza Hosseini: Added `~greykite.detection.detector.reward.Reward` that allows users to specify and optimize robust anomaly detection objectives.
* Sayan Patra: Added ``GreykiteDetector`` module that builds anomaly detection based on Greykite forecasting.
* Sayan Patra: Added tutorials for Greykite anomaly detection.

* New features and methods
* Reza Hosseini: Added `~greykite.common.features.outlier.ZScoreOutlierDetector` and `~greykite.common.features.outlier.TukeyOutlierDetector`, improved outlier detection modules.
* Sayan Patra: Added `~greykite.detection.common.pickler.GreykitePickler`. This improves the pickling function for Greykite models and allows to store the model in a single file.
* Yi-Wei Lu: Added ``DifferenceBasedOutlierTransformer`` that can identify outliers in the ``sklearn`` pipeline.

* Library enhancements
* Kaixu Yang: Added ``scipy`` solver to make quantile regression more stable.
* Qiang Fei: Updated ``auto_holiday`` functionality to use holiday groupers for improved forecast performance in holiday periods.
* Katherine Li: Improved changepoint detection method that can identify level shifts.

* Bug fixes
* Reza Hosseini Sayan Patra Yi Su Qiang Fei Kaixu Yang Phil Gaudreau: Other library enhancements and bug fixes.

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Loosen dill package requirements.

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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.
* 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.
* 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.
* 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 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.

* 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.

* 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.

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

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* 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.

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* 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.