ITransformerHyperparams
chronax.models.itransformer_model.ITransformerHyperparams · inherits PydanticBaseModel
(No description provided)
__init__(self, **data: Any)
Initializes ITransformerHyperparams.
| Parameter | Type | Default | Description |
|---|---|---|---|
**data |
Any |
- | (undocumented) |
ITransformerModel
chronax.models.itransformer_model.ITransformerModel · inherits BaseModel
(No description provided)
__init__(self, params: Dict[str, Any], settings: Dict[str, Any])
Initializes the ITransformerModel.
| 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) -> "ITransformerModel"
(No description provided)
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).
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
(No description provided)
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.
Raises: ValueError