Quickstart

import pandas as pd
from omnicast import AutoForecaster

y = pd.Series(
    [112, 118, 121, 130, 128, 137, 143, 149, 154, 162, 169, 175],
    index=pd.period_range("2025-01", periods=12, freq="M"),
)

model = AutoForecaster(
    seasonal_period=None,
    metric="rmse",
    validation_horizon=1,
).fit(y)

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

The pieces

Input. y is a pandas.Series with a PeriodIndex, a regular-frequency DatetimeIndex, a RangeIndex, or a plain numeric index. Models never impute missing values or guess an irregular frequency for you – fit raises instead.

Fitting. model.fit(y) learns parameters and populates trailing-underscore attributes:

• fitted_values_, residuals_, sigma2_ – always present.

• params_, parameter_confidence_intervals_, aic_, bic_ – statistical estimators only (ETSForecaster, ARIMAForecaster, AutoARIMAForecaster).

Prediction. model.predict(horizon, level=[80, 95]) returns a ForecastResult: a mean series plus a lower/upper series per requested coverage level. level accepts a single number or an iterable; intervals are computed fresh on every call because they depend on horizon and coverage.

forecast.mean            # point forecast, pandas.Series
forecast.interval(95)    # DataFrame with lower/upper columns for one level
forecast.to_frame()      # mean + every requested interval, one DataFrame

Model selection. AutoForecaster fits a panel of candidates, scores each with rolling-origin backtesting (backtest()), and refits the winner on the full series. Inspect model.leaderboard_ to see every candidate’s score (candidates that raised an exception show score = inf and the error message, rather than aborting selection).

Choosing a model

If you need…	Reach for…
A zero-assumption baseline	NaiveForecaster
A baseline for seasonal data	SeasonalNaiveForecaster
A stable low-variance baseline	MeanForecaster
A trending baseline, cheap to compute	DriftForecaster
A strong, fast, seasonality-aware default	ThetaForecaster
Explicit control of error/trend/seasonal structure	ETSForecaster
ARIMA/SARIMA with a known or fixed order, or exogenous regressors	ARIMAForecaster
ARIMA with the order selected for you	AutoARIMAForecaster
A nonlinear/neural model for longer series	LSTMForecaster
Not having to choose at all	AutoForecaster

See Evaluation for backtesting and accuracy metrics.