TimesNetHyperparams

timesnet_model.TimesNetHyperparams · inherits PydanticBaseModel

Tunable training knobs; aligned with TFT / shorter GPU Batch runs.

Attribute	Type	Default	Description
input_size	int	128	NeuralForecast input_size (lookback).
max_steps	int	25	Lightning optimization steps per fit (not forecast horizon; tasks cap h at 128).
batch_size	int	64	Minibatch size (series per step); larger improves GPU throughput until memory-bound.

TimesNetModel

timesnet_model.TimesNetModel · inherits BaseModel

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

Initializes the TimesNet model, validating hyperparameters and checking for neuralforecast installation.

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) -> TimesNetModel

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: TimesNetModel (the fitted 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

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 (Forecast predictions). Raises: ValueError (if TimesNetModel is not fitted).