BATS

BATS forecaster for time series with multiple seasonality.

Quickstart

from sktime.forecasting.bats import BATS

estimator = BATS(use_box_cox=None, box_cox_bounds=(0, 1), use_trend=None, use_damped_trend=None, sp=None, use_arma_errors=True, show_warnings=True, n_jobs=None, multiprocessing_start_method='spawn', context=None)

Parameters(10)

use_box_cox: bool or None, optional (default=None)

If Box-Cox transformation of original series should be applied. When None both cases shall be considered and better is selected by AIC.

box_cox_bounds: tuple, shape=(2,), optional (default=(0, 1))

Minimal and maximal Box-Cox parameter values.

use_trend: bool or None, optional (default=None)

Indicates whether to include a trend or not. When None both cases shall be considered and better is selected by AIC.

use_damped_trend: bool or None, optional (default=None)

Indicates whether to include a damping parameter in the trend or not. Applies only when trend is used. When None both cases shall be considered and better is selected by AIC.

sp: Iterable or array-like of floats, optional (default=None)

Abbreviation of “seasonal periods”. The length of each of the periods (amount of observations in each period). Accepts int and float values here. When None or empty array, non-seasonal model shall be fitted.

use_arma_errors: bool, optional (default=True)

When True BATS will try to improve the model by modelling residuals with ARMA. Best model will be selected by AIC. If False, ARMA residuals modeling will not be considered.

show_warnings: bool, optional (default=True)

If warnings should be shown or not. Also see Model.warnings variable that contains all model related warnings.

n_jobs: int, optional (default=None)

How many jobs to run in parallel when fitting BATS model. When not provided BATS shall try to utilize all available cpu cores.

multiprocessing_start_method: str, optional (default=’spawn’)

How threads should be started. See also:

https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods

context: abstract.ContextInterface, optional (default=None)

For advanced users only. Provide this to override default behaviors

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.bats import BATS
>>> y = load_airline ()
>>> forecaster = BATS (
... use_box_cox = False,
... use_trend = False,
... use_damped_trend = False,
... sp = 12,
... use_arma_errors = False,
... n_jobs = 1)
>>> forecaster. fit (y) BATS(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

References

1. [1 ] (1, 2) https://github.com/intive-DataScience/tbats [2 ] De Livera, A.M., Hyndman, R.J., & Snyder, R. D. (2011), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513-1527. DOI: https://doi.org/10.1198/jasa.2011.tm09771 [3 ] Skorupa. Multiple Seasonalities using TBATS in Python. https://medium.com/intive-developers/forecasting-time-series-with-multiple-seasonalities-using-tbats-in-python-398a00ac0e8a [4 ] R.J. Hyndman, G. Athanasopoulos. Forecasting: Principles and Practice. https://otexts.com/fpp2/complexseasonality.html