Source code for lightwood.mixer.arima

from typing import Dict, Optional

from lightwood.mixer.sktime import SkTime

[docs]class ARIMAMixer(SkTime): def __init__( self, stop_after: float, target: str, dtype_dict: Dict[str, str], horizon: int, ts_analysis: Dict, model_path: str = 'statsforecast.StatsForecastAutoARIMA', auto_size: bool = True, sp: int = None, use_stl: bool = False, start_p: int = 2, d: Optional[int] = None, start_q: int = 2, max_p: int = 5, max_d: int = 2, max_q: int = 5, start_P: int = 1, D: Optional[int] = None, start_Q: int = 1, max_P: int = 2, max_D: int = 1, max_Q: int = 2, max_order: int = 5, seasonal: bool = True, stationary: bool = False, information_criterion: Optional[str] = None, alpha: float = 0.05, test: str = 'kpss', seasonal_test: Optional[str] = None, stepwise: bool = True, n_jobs: Optional[int] = None, start_params: Optional[list] = None, trend: Optional[str] = None, method: Optional[str] = None, maxiter: int = 50, offset_test_args: Optional[dict] = None, seasonal_test_args: Optional[dict] = None, suppress_warnings: bool = True, error_action: str = 'warn', trace: bool = False, random: bool = False, random_state: Optional[int] = None, n_fits: Optional[int] = None, out_of_sample_size: int = 0, scoring: str = 'mse', scoring_args: Optional[dict] = None, with_intercept: bool = True, update_pdq: bool = True, time_varying_regression: bool = False, enforce_stationarity: bool = True, enforce_invertibility: bool = True, simple_differencing: bool = False, measurement_error: bool = False, mle_regression: bool = True, hamilton_representation: bool = False, concentrate_scale: bool = False, ): """ Wrapper for SkTime's AutoARIMA 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 = { 'start_p': start_p, 'd': d, 'start_q': start_q, 'max_p': max_p, 'max_d': max_d, 'max_q': max_q, 'start_P': start_P, 'D': D, 'start_Q': start_Q, 'max_P': max_P, 'max_D': max_D, 'max_Q': max_Q, 'max_order': max_order, 'seasonal': seasonal, 'stationary': stationary, 'information_criterion': information_criterion, 'alpha': alpha, 'test': test, 'seasonal_test': seasonal_test, 'stepwise': stepwise, 'n_jobs': n_jobs, 'start_params': start_params, 'trend': trend, 'method': method, 'maxiter': maxiter, 'offset_test_args': offset_test_args, 'seasonal_test_args': seasonal_test_args, 'suppress_warnings': suppress_warnings, 'error_action': error_action, 'trace': trace, 'random': random, 'random_state': random_state, 'n_fits': n_fits, 'out_of_sample_size': out_of_sample_size, 'scoring': scoring, 'scoring_args': scoring_args, 'with_intercept': with_intercept, 'update_pdq': update_pdq, 'time_varying_regression': time_varying_regression, 'enforce_stationarity': enforce_stationarity, 'enforce_invertibility': enforce_invertibility, 'simple_differencing': simple_differencing, 'measurement_error': measurement_error, 'mle_regression': mle_regression, 'hamilton_representation': hamilton_representation, 'concentrate_scale': concentrate_scale, } super().__init__(stop_after, target, dtype_dict, horizon, ts_analysis, model_path, model_kwargs, auto_size, sp, hyperparam_search, use_stl) = 'AutoARIMA' self.stable = False