ARIMAForecaster¶

class omnicast.ARIMAForecaster(order=(1, 0, 0), seasonal_order=(0, 0, 0, 0), trend=None)[source]

Bases: BaseForecaster

ARIMA/SARIMA model with optional exogenous regressors.

Examples

>>> import pandas as pd
>>> from omnicast import ARIMAForecaster
>>> y = pd.Series([10.0, 12.0, 11.0, 13.0, 15.0, 14.0])
>>> model = ARIMAForecaster(order=(1, 0, 0)).fit(y)
>>> model.predict(horizon=2).mean.round(2).tolist()
[14.76, 15.55]

Pass seasonal_order=(P, D, Q, m) for SARIMA, and an X DataFrame (with matching index at both fit and predict time) to include exogenous regressors.

Notes

When to use this model¶
Best for	You already know (or want to fix) the ARIMA/SARIMA order, or need exogenous regressors – the only model in this package that supports them
Avoid when	You want the order searched automatically (see `AutoARIMAForecaster`)
Handles trend	Yes, via differencing (`d`) and/or `trend`
Handles seasonality	Yes, via `seasonal_order`
Extra dependencies	None (uses `statsmodels`)
Min. observations	Enough for `SARIMAX` to estimate the requested order; roughly `sum(order) + sum(seasonal_order[:3]) + 1` at minimum

Parameters:

order (tuple[int, int, int])
seasonal_order (tuple[int, int, int, int])
trend (str | None)

fit(y, X=None)[source]

predict(horizon, X=None, level=(80, 95))[source]

fit_predict(y, horizon, **kwargs)

Return type:

ForecastResult

Parameters:

y (Iterable[float] | Series)
horizon (int)
kwargs (object)

get_params()

Return type:: dict[str, object]

A direct wrapper around statsmodels.tsa.statespace.sarimax.SARIMAX. Use it when you already know (or want to fix) the ARIMA order – for a searched order, see AutoARIMAForecaster.

from omnicast import ARIMAForecaster

model = ARIMAForecaster(order=(2, 1, 1)).fit(y)
forecast = model.predict(horizon=6, level=[80, 95])
print(forecast.to_frame())

           mean  lower_80  upper_80  lower_95  upper_95
2026-01  201.99    196.56    207.42    193.68    210.29
2026-02  204.43    194.68    214.18    189.52    219.34
2026-03  206.33    192.07    220.58    184.53    228.12
2026-04  207.46    189.04    225.88    179.29    235.64
2026-05  208.30    185.94    230.65    174.11    242.49
2026-06  208.82    182.83    234.81    169.07    248.57

order=(2, 1, 1) alone can’t represent the series’ yearly seasonality (note the wide, fast-growing intervals compared to ETSForecaster); pass seasonal_order=(P, D, Q, m) for SARIMA:

model = ARIMAForecaster(
    order=(1, 1, 1),
    seasonal_order=(1, 1, 1, 12),
).fit(y)

Exogenous regressors¶

Unlike most models in this package, ARIMAForecaster supports exogenous regressors – pass a X with a matching index at both fit and predict time (future values for the forecast horizon must be supplied; the model will not extrapolate them for you):

model = ARIMAForecaster(order=(1, 0, 0)).fit(y, X=X_train)
forecast = model.predict(horizon=6, X=X_future)

Fitted statistical attributes are available exactly as with ETSForecaster: params_, parameter_confidence_intervals_, aic_, bic_.