WindowAverage

chronax.models.WindowAverage · inherits BaseForecaster

Uses the average of the last $k$ observations, with $k$ the length of the window. Wider windows will capture global trends, while narrow windows will reveal local trends. The length of the window selected should take into account the importance of past observations and how fast the series changes.

__init__(self, window_size: int, alias: str = 'WindowAverage', conformal_params: Optional[ConformalIntervals] = None) -> None

Initializes the WindowAverage model.

Parameter	Type	Default	Description
window_size	int	-	Size of truncated series on which average is estimated.
alias	str	"WindowAverage"	Custom name of the model.
conformal_params	Optional[ConformalIntervals]	None	Information to compute conformal prediction intervals. This is required for generating future prediction intervals.

fit(self, y: jnp.ndarray, X: Optional[jnp.ndarray] = None) -> Self

Fit the WindowAverage model.

Fit an WindowAverage to a time series (numpy array) y and optionally exogenous variables (numpy array) X.

Parameters:

Parameter	Type	Default	Description
y	jnp.ndarray	-	Clean time series of shape (t, ).
X	Optional[jnp.ndarray]	None	Optional exogenous of shape (t, n_x).

Returns: Self (WindowAverage fitted model. Sets self.model_).

predict(self, h: int, X: Optional[jnp.ndarray] = None, level: Optional[List[int]] = None) -> Dict[str, jnp.ndarray]

Predict with fitted WindowAverage.

Parameters:

Parameter	Type	Default	Description
h	int	-	Forecast horizon.
X	Optional[jnp.ndarray]	None	Optional exogenous of shape (h, n_x).
level	Optional[List[int]]	None	Confidence levels (0-100) for prediction intervals.

Returns: dict (Dictionary with entries mean for point predictions and level_* for probabilistic predictions.)

Return Keys: * mean: jnp.ndarray * lo-L, hi-L: jnp.ndarray (If level is provided, where L is the confidence level)

Raises: * ValueError: If level is requested but conformal_params is None. * ValueError: If level is requested but conformity scores are not available (model not fitted with conformal_params).

forecast(self, y: jnp.ndarray, h: int, X: Optional[jnp.ndarray] = None, X_future: Optional[jnp.ndarray] = None, level: Optional[List[int]] = None, fitted: bool = False) -> Dict[str, jnp.ndarray]

Memory Efficient WindowAverage predictions.

This method avoids memory burden due from object storage. It is analogous to fit_predict without storing information. It assumes you know the forecast horizon in advance.

Parameters:

Parameter	Type	Default	Description
y	jnp.ndarray	-	Clean time series of shape (n, ).
h	int	-	Forecast horizon.
X	Optional[jnp.ndarray]	None	Optional insample exogenous of shape (t, n_x).
X_future	Optional[jnp.ndarray]	None	Optional exogenous of shape (h, n_x).
level	Optional[List[int]]	None	Confidence levels (0-100) for prediction intervals.
fitted	bool	False	Whether or not to return insample predictions.

Returns: dict (Dictionary with entries mean for point predictions and level_* for probabilistic predictions.)

Return Keys: * mean: jnp.ndarray * lo-L, hi-L: jnp.ndarray (If level is provided, where L is the confidence level)

Raises: * Exception: If level is requested but conformal_params is None.