ARIMA#

class oats.models.predictive.arima.ARIMAModel(p=1, d=1, q=1, **kwargs)[source]#

Bases: Model

Autoregressive Intergrated Moving Average Model

Implemented using statsmodels package. Multivariate scoring enabled by fitting and predicting each feature column.

Common parameters:

  • ARIMA(1,0,0) = first-order autoregressive model

  • ARIMA(0,1,0) = random walk

  • ARIMA(1,1,0) = differenced first-order autoregressive model

  • ARIMA(0,1,1) without constant = simple exponential smoothing

  • ARIMA(0,1,1) with constant = simple exponential smoothing with growth

  • ARIMA(0,2,1) or (0,2,2) without constant = linear exponential smoothing

  • ARIMA(1,1,2) with constant = damped-trend linear exponential smoothing`

Parameters:

  • p (int, optional) – _description_. Defaults to 1.

  • d (int, optional) – _description_. Defaults to 1.

  • q (int, optional) – _description_. Defaults to 1.

fit(train, *args, **kwargs)[source]#
get_scores(data)[source]#