VarmaxHyperparams

chronax.models.varmax_model.VarmaxHyperparams · inherits PydanticBaseModel

Pydantic model defining the hyperparameters for the VARMAX model.

Attributes

Attribute	Type	Default	Description
p	int	-	Number of AR parameters
q	int	-	Number of MA parameters
trend	Literal["n", "c", "t", "ct"]	"c"	Deterministic trend: 'n' none, 'c' constant, 't' linear, 'ct' both

VarmaxModel

chronax.models.varmax_model.VarmaxModel · inherits BaseModel

Multivariate VARMAX model.

__init__(self, params: Dict[str, Any], settings: Dict[str, Any])

Initializes the VARMAX model wrapper.

Parameter	Type	Default	Description
params	Dict[str, Any]	-	(undocumented)
settings	Dict[str, Any]	-	(undocumented)

train(self, y_context: np.ndarray, y_target: np.ndarray, timestamps_context: np.ndarray, timestamps_target: np.ndarray, x_context: Optional[np.ndarray] = None, x_target: Optional[np.ndarray] = None, **kwargs: dict) -> VarmaxModel

Train VARMAX on context + train targets (and optional stacked covariates).

Parameter	Type	Default	Description
y_context	np.ndarray	-	(undocumented)
y_target	np.ndarray	-	(undocumented)
timestamps_context	np.ndarray	-	(undocumented)
timestamps_target	np.ndarray	-	(undocumented)
x_context	Optional[np.ndarray]	None	(undocumented)
x_target	Optional[np.ndarray]	None	(undocumented)
**kwargs	dict	-	(undocumented)

Returns: VarmaxModel (the fitted forecaster; sets self._model).

predict(self, y_context: np.ndarray, timestamps_context: np.ndarray, timestamps_target: np.ndarray, x_context: Optional[np.ndarray] = None, x_target: Optional[np.ndarray] = None, **kwargs: dict) -> np.ndarray

Forecast using the fitted VARMAX model (first num_targets columns only).

Parameter	Type	Default	Description
y_context	np.ndarray	-	(undocumented)
timestamps_context	np.ndarray	-	(undocumented)
timestamps_target	np.ndarray	-	(undocumented)
x_context	Optional[np.ndarray]	None	(undocumented)
x_target	Optional[np.ndarray]	None	(undocumented)
**kwargs	dict	-	(undocumented)

Returns: np.ndarray.