Moirai2Model
moirai2_model.Moirai2Model
Moirai 2.0 foundation model wrapper. Moirai 2.0 is decoder-only, uses quantile forecasts, and supports past + future covariates. Native multivariate.
__init__(self, params: Dict[str, Any], settings: Dict[str, Any])
(No prose summary provided.)
| 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) -> Moirai2Model
Initialize Moirai 2.0 (no training required for foundation models).
| 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: Moirai2Model (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
Make predictions using Moirai 2.0.
| 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 (samples of shape (num_samples, forecast_horizon, num_targets)).