smooth.ES.ic_weights

property ES.ic_weights: Dict[str, float]

$ICw).

Akaike weights represent the relative likelihood of each model being the best model given the data. They are used to combine forecasts from multiple models.

Returns:

Dictionary mapping model names to their IC weights. Weights sum to 1.0.

Return type:

Dict[str, float]

Raises:

ValueError – If the model has not been fitted or was not fitted with combination.

Examples

>>> model = ADAM(model="CCC", lags=[1])
>>> model.fit(y)
>>> weights = model.ic_weights
>>> print(f"ANN weight: {weights.get('ANN', 0):.3f}")

Type:

Return IC weights for combined models (R