smooth.ES.predict

ES.predict(h, X=None, interval='none', level=0.95, side='both', cumulative=False, nsim=10000, occurrence=None, scenarios=False)

Generate forecasts using the fitted ADAM model.

Matches R’s forecast.adam() interface for interval types and additional parameters.

Parameters:

  • h (int) – Forecast horizon (number of steps to forecast).

  • X (Optional[NDArray], default=None) – Exogenous variables for the forecast period. Ensure that X covers the entire forecast horizon h.

  • interval (str, default="none") –

    Type of prediction interval to construct:

    • "none": No intervals, point forecasts only.

    • "prediction": Automatically selects "simulated" or "approximate" depending on the model type.

    • "simulated": Simulation-based intervals (Monte Carlo).

    • "approximate": Analytical (parametric) intervals.

    • "semiparametric": Not yet implemented.

    • "nonparametric": Not yet implemented.

    • "empirical": Not yet implemented.

    • "confidence": Not yet implemented.

    • "complete": Not yet implemented.

  • level (float or list of float, default=0.95) –

    Confidence level(s) for prediction intervals. Accepts a single value (e.g. 0.95) or a list for multiple simultaneous levels (e.g. [0.9, 0.95, 0.99]). Values above 1 are treated as percentages and divided by 100.

    Each level produces a pair of lower_X / upper_X columns in the output, where X is the corresponding quantile. For example, level=0.95 with side="both" yields columns "lower_0.025" and "upper_0.975".

  • side (str, default="both") –

    Which side(s) of the intervals to compute:

    • "both": Both lower and upper bounds (default).

    • "upper": Upper bound only. Column named "upper_<level>".

    • "lower": Lower bound only. Column named "lower_<1-level>".

  • cumulative (bool, default=False) – If True, return cumulative (summed) forecasts over the horizon.

  • nsim (int, default=10000) – Number of simulations for simulation-based intervals.

  • occurrence (Optional[NDArray], default=None) – External occurrence probabilities for the forecast period. Overrides the fitted model’s occurrence for forecasting.

  • scenarios (bool, default=False) – If True and interval="simulated", store the raw simulation matrix in self._forecast_results["scenarios"].

Returns:

DataFrame with "mean" column and optional interval columns.

Return type:

pd.DataFrame

Raises:

ValueError – If the model has not been fitted yet or h is not set.

Parent Class: ES