AutoARIMAForecaster

class omnicast.AutoARIMAForecaster(seasonal_period=None, max_p=2, max_d=1, max_q=2, max_P=1, max_D=1, max_Q=1, information_criterion='aicc')[source]

Bases: ARIMAForecaster

Select a non-seasonal or seasonal ARIMA by corrected AIC grid search.

Examples

>>> import pandas as pd
>>> from omnicast import AutoARIMAForecaster
>>> y = pd.Series([10.0, 12.0, 11.0, 13.0, 15.0, 14.0])
>>> model = AutoARIMAForecaster(max_p=1, max_d=1, max_q=0).fit(y)
>>> model.order_
(0, 1, 0)
>>> model.predict(horizon=2).mean.round(2).tolist()
[14.0, 14.0]

Pass seasonal_period to also search (P, D, Q, m).

Notes

When to use this model

Best for

You want ARIMA/SARIMA without hand-picking an order; the default first candidate AutoForecaster tries

Avoid when

Interactive use with a wide search space – cost scales as the product of every max_* bound plus one, and it is often the slowest model in the package to fit

Handles trend

Yes, searched via max_d

Handles seasonality

Yes, searched via seasonal_period + max_P/max_D/max_Q

Extra dependencies

None (uses statsmodels)

Min. observations

Enough for the largest candidate order to be estimated; failed candidates are skipped rather than aborting the search
Parameters:

  • seasonal_period (int | None)

  • max_p (int)

  • max_d (int)

  • max_q (int)

  • max_P (int)

  • max_D (int)

  • max_Q (int)

  • information_criterion (str)

fit(y, X=None)[source]
fit_predict(y, horizon, **kwargs)
Return type:

ForecastResult

Parameters:
get_params()
Return type:

dict[str, object]

predict(horizon, X=None, level=(80, 95))

Grid-searches (p, d, q) and, if seasonal_period is given, (P, D, Q, m) too, fitting a SARIMAX for every combination and keeping the one with the best information criterion ("aicc" by default – corrected AIC, more reliable than raw AIC on short series). Failed fits (non-convergent, singular) are silently skipped rather than aborting the search.

from omnicast import AutoARIMAForecaster

model = AutoARIMAForecaster(
    seasonal_period=12,
    max_p=2, max_d=1, max_q=2,
    max_P=1, max_D=1, max_Q=1,
    information_criterion="aicc",
).fit(y)

print("chosen order:", model.order_, "seasonal_order:", model.seasonal_order_)
print(model.search_results_.head(5))

chosen order: (0, 1, 2) seasonal_order: (0, 1, 1, 12)
       order seasonal_order    aic   aicc    bic
0  (0, 1, 2)  (0, 1, 1, 12)  73.75  74.68  77.73
1  (2, 0, 2)  (1, 1, 1, 12)  90.70  93.50  98.01
2  (0, 1, 2)  (1, 1, 1, 12)  92.28  93.71  97.26
3  (1, 0, 2)  (0, 1, 1, 12)  92.82  94.25  98.04
4  (0, 1, 1)  (0, 1, 1, 12)  94.10  94.64  97.23

search_results_ is sorted by information_criterion – the chosen (0, 1, 2)(0, 1, 1, 12) beats the runner-up by a wide AICc margin here, because it correctly captures both the trend (d=1) and yearly seasonal differencing (D=1).

forecast = model.predict(horizon=6, level=[80, 95])
print(forecast.to_frame())

           mean  lower_80  upper_80  lower_95  upper_95
2026-01  207.64    206.13    209.15    205.33    209.95
2026-02  215.19    213.46    216.92    212.54    217.84
2026-03  219.77    218.00    221.55    217.05    222.50
2026-04  224.87    223.04    226.71    222.07    227.68
2026-05  225.02    223.14    226.91    222.15    227.90
2026-06  223.05    221.12    224.98    220.10    226.00

AutoARIMAForecaster subclasses ARIMAForecaster, so after fit it exposes the same params_, aic_, bic_, and prediction API – plus order_/seasonal_order_ (the winning search result) and search_results_ (every candidate tried).

Cost

The search space is (max_p+1) * (max_d+1) * (max_q+1) * (max_P+1) * (max_D+1) * (max_Q+1) SARIMAX fits. Keep the max_* bounds small for interactive use, or expect this to take noticeably longer than any other model in the package – it’s also why it is the default first candidate AutoForecaster tries, but the one most likely to dominate its total runtime.