TimeMoe50mHyperparams

chronax.models.TimeMoe50mHyperparams · inherits PydanticBaseModel

__init__(self)

(No parameters)

TimeMoe50mModel

chronax.models.TimeMoe50mModel · inherits BaseForecaster

Time-MoE mixture-of-experts foundation model for time series forecasting. Time-MoE produces deterministic (point) forecasts via autoregressive generation. It operates on univariate sequences, so multivariate targets are handled by iterating over each target independently with mean/std normalization.

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

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) -> "TimeMoe50mModel"

Parameters:

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		-	(undocumented)

Returns: Self (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) -> np.ndarray

Parameters:

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		-	(undocumented)

Returns: np.ndarray (The predicted point forecasts). Raises: ValueError