Source code for lightwood.mixer.ets

from typing import Dict, Optional

from lightwood.mixer.sktime import SkTime

[docs]class ETSMixer(SkTime): def __init__( self, stop_after: float, target: str, dtype_dict: Dict[str, str], horizon: int, ts_analysis: Dict, model_path: str = 'ets.AutoETS', auto_size: bool = True, sp: int = None, use_stl: bool = True, error: str = 'add', trend: Optional[str] = None, damped_trend: bool = False, seasonal: Optional[str] = None, initialization_method: str = 'estimated', initial_level: Optional[float] = None, initial_trend: Optional[float] = None, initial_seasonal: Optional[list] = None, bounds: Optional[dict] = None, start_params: Optional[list] = None, maxiter: int = 1000, auto: bool = False, information_criterion: str = 'aic', allow_multiplicative_trend: bool = False, restrict: bool = True, additive_only: bool = False, ignore_inf_ic: bool = True, n_jobs: Optional[int] = None, random_state: Optional[int] = None ): """ Wrapper for SkTime's AutoETS interface. :param stop_after: time budget in seconds :param target: column containing target time series :param dtype_dict: data types for each dataset column :param horizon: forecast length :param ts_analysis: lightwood-produced stats about input time series :param auto_size: whether to filter out old data points if training split is bigger than a certain threshold (defined by the dataset sampling frequency). Enabled by default to avoid long training times in big datasets. :param use_stl: Whether to use de-trenders and de-seasonalizers fitted in the timeseries analysis phase. For the rest of the parameters, please refer to SkTime's documentation. """ # noqa hyperparam_search = False model_kwargs = { 'error': error, 'trend': trend, 'damped_trend': damped_trend, 'seasonal': seasonal, 'initialization_method': initialization_method, 'initial_level': initial_level, 'initial_trend': initial_trend, 'initial_seasonal': initial_seasonal, 'bounds': bounds, 'start_params': start_params, 'maxiter': maxiter, 'auto': auto, 'information_criterion': information_criterion, 'allow_multiplicative_trend': allow_multiplicative_trend, 'restrict': restrict, 'additive_only': additive_only, 'ignore_inf_ic': ignore_inf_ic, 'n_jobs': n_jobs, 'random_state': random_state } super().__init__(stop_after, target, dtype_dict, horizon, ts_analysis, model_path, model_kwargs, auto_size, sp, hyperparam_search, use_stl) = 'AutoETS' self.stable = False