AutoETS

chronax.models.AutoETS · inherits BaseForecaster

Automatic Exponential Smoothing model. Automatically selects the best ETS (Error, Trend, Seasonality) model using an information criterion. Default is Akaike Information Criterion (AICc), while particular models are estimated using maximum likelihood. The state-space equations can be determined based on their $M$ multiplicative, $A$ additive, $Z$ optimized or $N$ ommited components. The model string parameter defines the ETS equations: E in [$M, A, Z$], T in [$N, A, M, Z$], and S in [$N, A, M, Z$].

__init__(self, season_length=1, model='ZZZ', damped=None, phi=None, max_iter=None, optax_lr=7e-2, optax_clip=5.0, early_stop_patience=10, early_stop_min_delta=1e-05, alias='AutoETS', prediction_intervals=None)

Initialize the AutoETS estimator configuration.

Parameter	Type	Default	Description
season_length	int	1	Number of observations per unit of time. Ex: 24 Hourly data.
model	str	"ZZZ"	Controlling state-space-equations.
damped	Optional[bool]	None	A parameter that 'dampens' the trend.
phi	Optional[float]	None	Smoothing parameter for trend damping. Only used when damped=True.
max_iter	Optional[int]	None	(undocumented)
optax_lr	float	7e-2	(undocumented)
optax_clip	float	5.0	(undocumented)
early_stop_patience	int	10	(undocumented)
early_stop_min_delta	float	1e-5	(undocumented)
alias	str	"AutoETS"	Custom name of the model.
prediction_intervals	Optional[ConformalIntervals]	None	Information to compute conformal prediction intervals. By default, the model will compute the native prediction intervals.

fit(self, y, X=None) -> Self

Fit the Exponential Smoothing model.

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

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 (The fitted forecaster; sets self.model_.)

predict(self, h, X=None, level=None) -> dict[str, jnp.ndarray]

Predict with fitted Exponential Smoothing.

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[str, jnp.ndarray] (Dictionary with entries mean for point predictions and level_* for probabilistic predictions.) Return Keys: * mean: jnp.ndarray * lo-L: jnp.ndarray (if level is provided) * hi-L: jnp.ndarray (if level is provided)

predict_in_sample(self, level=None) -> dict[str, jnp.ndarray]

Access fitted Exponential Smoothing insample predictions.

Parameter	Type	Default	Description
level	Optional[List[int]]	None	Confidence levels (0-100) for prediction intervals.

Returns: dict[str, jnp.ndarray] (Dictionary with entries fitted for point predictions and level_* for probabilistic predictions.) Return Keys: * fitted: jnp.ndarray * fitted-lo-L: jnp.ndarray (if level is provided) * fitted-hi-L: jnp.ndarray (if level is provided)

forecast(self, y, h, X=None, X_future=None, level=None, fitted=False) -> dict[str, Any]

Memory Efficient Exponential Smoothing 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
y	jnp.ndarray	-	Clean time series of shape (n, ).
h	int	-	Forecast horizon.
X	Optional[jnp.ndarray]	None	Optional insample exogenpus 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 returns insample predictions.

Returns: dict[str, Any] (Dictionary with entries mean for point predictions and level_* for probabilistic predictions.)

forward(self, y, h, X=None, X_future=None, level=None, fitted=False) -> dict[str, Any]

Apply fitted Exponential Smoothing model to a new time series.

Parameter	Type	Default	Description
y	jnp.ndarray	-	Clean time series of shape (n, ).
h	int	-	Forecast horizon.
X	Optional[jnp.ndarray]	None	Optional insample exogenpus 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 for prediction intervals.
fitted	bool	False	Whether or not to return insample predictions.

Returns: dict[str, Any] (Dictionary with entries mean for point predictions and level_* for probabilistic predictions.)