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Forecaster

GreykiteForecaster

API-ReferenzQuellcode
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Adapter for using Greykite forecasting models within sktime.

This forecaster wraps Greykite forecast_pipeline and exposes a sktime-compatible API.

WARNING: the greykite package has very restrictive dependencies that typically prevent installation together with other packages. For this reason, this estimator is also not covered by regular tests. We therefore recommend to run check_estimator(GreykiteForecaster) on your system before deploying this estimator.

Schnellstart

python
from sktime.forecasting.greykite import GreykiteForecaster

estimator = GreykiteForecaster(forecast_config: GreykiteForecaster.ForecastConfig | None=None, date_format: str | None=None, model_template: str='SILVERKITE', coverage: float=0.95, forecast_one_by_one=None, freq=None, anomaly_info=None, model_components_param=None, evaluation_metric_param=None, evaluation_period_param=None, computation_param=None)

Parameter(11)

forecast_configgreykite ForecastConfig or None, default=None

Optional Greykite ForecastConfig used as the base configuration. If None, a config is built from the flattened parameters below. Prefer the flattened parameters for discoverability; use forecast_config for advanced setups or when migrating existing Greykite code.

date_formatstr or None, default=None

Format string for parsing timestamps (e.g. "%Y-%m-%d"). If None, Greykite infers the format.

model_templatestr, default=”SILVERKITE”

Greykite model template name. Used only when forecast_config is None; otherwise set forecast_config.model_template instead.

Main templates:

  • "SILVERKITE" - default Silverkite model with automatic growth, seasonality, holidays, autoregression, and interactions. Good general choice for hourly and daily data.

  • "PROPHET" - Facebook Prophet with growth, seasonality, holidays, regressors, and prediction intervals.

  • "AUTO_ARIMA" - ARIMA with automatic order selection.

  • "AUTO" - automatically selects a SimpleSilverkite template from data frequency, forecast horizon, and CV settings.

  • "SILVERKITE_EMPTY" - intercept-only Silverkite; add only the components you need via model_components_param.

  • "SK" - low-level Silverkite interface for custom tuning; not intended out-of-the-box.

  • "LAG_BASED" - forecasts from aggregated past values (e.g. week-over-week).

  • "SILVERKITE_TWO_STAGE" / "SILVERKITE_WOW" - multistage models (long-term effects then short-term residuals, or Silverkite plus week-over-week).

Frequency / horizon variants of Silverkite also exist, for example SILVERKITE_DAILY_1, SILVERKITE_DAILY_90, SILVERKITE_WEEKLY, SILVERKITE_MONTHLY, and SILVERKITE_HOURLY_{1,24,168,336}. These use hyperparameters tuned for that frequency and horizon. See greykite ModelTemplateEnum for the full list [2].

coveragefloat, default=0.95

Intended coverage of prediction bands, between 0 and 1. Used only when forecast_config is None; otherwise set forecast_config.coverage instead. If None on the config, Greykite does not return upper/lower prediction bounds.

forecast_one_by_onebool, int, list of int, or None, default=None

If set, enables Greykite’s one-by-one forecasting (fit/predict in segments of the horizon). True uses the full horizon as one segment; an int is a segment size; a list of ints must sum to the horizon. If None and forecast_config is None, defaults to False; if forecast_config is provided, that config’s value is kept.

freqstr or None, default=None

Pandas frequency string for the series (e.g. "D", "H"). If None, inferred from the index of y when possible. Maps to MetadataParam.freq.

anomaly_infodict, list of dict, or None, default=None

Optional anomaly adjustment specification. Values flagged here are corrected before fitting. Maps to MetadataParam.anomaly_info.

model_components_paramdict, ModelComponentsParam, list, or None, default=None

Model structure and tuning options passed to Greykite ModelComponentsParam. If None, an empty ModelComponentsParam is used and the chosen model_template supplies component defaults. When a dict (or list of dicts for grid search), recognized keys are:

  • growthdict, default=None

    Trend / growth terms. Template default if omitted.

evaluation_metric_paramdict, EvaluationMetricParam, or None, default=None

Metrics used for CV reporting and model selection. Passed to Greykite EvaluationMetricParam. If None, an empty param object is used and Greykite pipeline defaults apply. When a dict, recognized keys are:

  • cv_selection_metricstr, default=”MeanAbsolutePercentError”

    Metric name used to pick the best CV model (EvaluationMetricEnum member name).

evaluation_period_paramdict, EvaluationPeriodParam, or None, default=None

Train/test and cross-validation split configuration. Passed to Greykite EvaluationPeriodParam. If None, an empty param object is used and Greykite pipeline defaults apply. When a dict, recognized keys are:

  • test_horizonint, default=forecast horizon

    Holdout length at the end of the series. Set to 0 to skip backtest. Greykite default when unset is the forecast horizon.

computation_paramdict, ComputationParam, or None, default=None

Runtime / parallelization options. Passed to Greykite ComputationParam. If None, an empty param object is used and Greykite pipeline defaults apply. When a dict, recognized keys are:

  • n_jobsint, default=1

    Parallel jobs for hyperparameter search (-1 uses all processors).

Beispiele

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.greykite import GreykiteForecaster
>>> from sktime.forecasting.base import ForecastingHorizon
>>> y = load_airline (). to_timestamp ()
>>> fh = ForecastingHorizon ([1, 2, 3 ])
>>> forecaster = GreykiteForecaster ()
>>> forecaster. fit (y = y, fh = fh)
>>> y_pred = forecaster. predict (fh = fh)

Referenzen