Trend Models

ARIMA

Smoothers

EWM

class ThymeBoost.trend_models.ewm_trend.EwmModel

Bases: TrendBaseModel

The ewm method utilizes a Pandas ewm method: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html. Fitting this with a ‘local’ fit_type parameter is not advised.

fit(y, **kwargs)

Fit the trend component in the boosting loop for a ewm model using the ‘ewm_alpha’ parameter.

Parameters:

  • time_series (np.ndarray) – DESCRIPTION.

  • **kwargs – The key ‘ewm_alpha’ is passed to this method from the ThymeBoost fit method.

Return type:

Fitted array.

model = 'ewm'
predict(forecast_horizon, model_params)

Linear

LOESS

class ThymeBoost.trend_models.loess_trend.LoessModel

Bases: TrendBaseModel

fit(y, **kwargs)

Fit the trend component in the boosting loop for a ewm model using alpha.

Parameters:

  • time_series (TYPE) – DESCRIPTION.

  • **kwargs (TYPE) – DESCRIPTION.

Return type:

None.

model = 'loess'
predict(forecast_horizon, model_params)

Mean

class ThymeBoost.trend_models.mean_trend.MeanModel

Bases: TrendBaseModel

fit(y, **kwargs)

Fit the trend component in the boosting loop for a mean model.

Parameters:

  • time_series (TYPE) – DESCRIPTION.

  • **kwargs (TYPE) – DESCRIPTION.

Return type:

None.

model = 'mean'
predict(forecast_horizon, model_params)

Median

class ThymeBoost.trend_models.median_trend.MedianModel

Bases: TrendBaseModel

fit(y, **kwargs)

Fit the trend component in the boosting loop for a mean model.

Parameters:

  • time_series (TYPE) – DESCRIPTION.

  • **kwargs (TYPE) – DESCRIPTION.

Return type:

None.

model = 'median'
predict(forecast_horizon, model_params)

RANSAC