RandomWalkWithDrift

chronax.models.randomWalkWithDrift.RandomWalkWithDrift · inherits BaseForecaster

The RandomWalkWithDrift class implements statsforecast's random walk with drift forecasting model. A variation of the naive method that allows forecasts to change over time by extrapolating a linear trend between the first and last observations.

Attributes

Name	Type	Description
alias	str	Alias for the model, defaults to "RWD".
conformal_params	ConformalIntervals \| None	Parameters for conformal prediction, if used.
model_	dict	Dict containing fitted parameters (slope, last_y, sigma, fitted values, n).

__init__(self, alias: str = 'RWD', conformal_params: ConformalIntervals | None = None)

Initializes the RandomWalkWithDrift model.

Parameter	Type	Default	Description
alias	str	"RWD"	(undocumented)
conformal_params	ConformalIntervals \| None	None	(undocumented)

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

Fit the RandomWalkWithDrift model.

Parameter	Type	Default	Description
y	jnp.ndarray	-	Clean time series of shape (t,)
X	jnp.ndarray \| None	None	Optional exogenous variables (not used, for API compatibility)

Returns: Self (the fitted forecaster; sets self.model_).

predict(self, h: int, level: list[int] | None = None) -> dict

Predict with fitted RandomWalkWithDrift.

Parameter	Type	Default	Description
h	int	-	Forecast horizon
level	list[int] \| None	None	Confidence levels (0-100) for prediction intervals

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

predict_in_sample(self, level: list[int] | None = None) -> dict

Access fitted RandomWalkWithDrift insample predictions.

Parameter	Type	Default	Description
level	list[int] \| None	None	Confidence levels (0-100) for prediction intervals

Returns: dict (Dictionary with entries fitted for point predictions).

forecast(self, h: int, y: jnp.ndarray, X: jnp.ndarray | None = None, X_future: jnp.ndarray | None = None, level: list[int] | None = None, fitted: bool = False) -> dict

Memory Efficient RandomWalkWithDrift 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.

Parameter	Type	Default	Description
h	int	-	Forecast horizon
y	jnp.ndarray	-	Clean time series of shape (n,)
X	jnp.ndarray \| None	None	Optional insample exogenous of shape (t, n_x) (not used, for API compatibility)
X_future	jnp.ndarray \| None	None	Optional exogenous of shape (h, n_x) (not used, for API compatibility)
level	list[int] \| None	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).