Chronos2Forecaster#

class openstef_foundation_models.models.forecasting.chronos2_forecaster.Chronos2Forecaster(**data: Any) → None[source]

Bases: Forecaster

Zero-shot probabilistic forecaster backed by a Chronos-2 checkpoint.

The forecaster composes an InferenceBackend (built once and reused across an entire backtest) and translates between OpenSTEF datasets and the model’s tensor interface. Prediction is batch-first: predict_batch() runs the backend once over a stack of series and predict() is a batch-of-one wrapper.

Parameters:: data (Any)

HyperParams: alias of Chronos2HyperParams

backend: InferenceBackend

hyperparams: Chronos2HyperParams

supports_batching: bool

property hparams: Chronos2HyperParams

Model hyperparameters for training and prediction.

Concrete forecasters implement this by returning their narrowed hyperparams field, giving callers a polymorphic read-only view while each subclass keeps full type safety on its own field.

property is_fitted: bool: Check if the predictor has been fitted.

fit(data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) → None[source]

Fit the forecaster.

Chronos-2 is pretrained and zero-shot, so there is nothing to fit. The method exists only to satisfy the forecaster contract.

Parameters:

data (ForecastInputDataset) – Unused training data.
data_val (ForecastInputDataset | None) – Unused validation data.
data
data_val

Return type:

None

predict(data: ForecastInputDataset) → ForecastDataset[source]

Forecast a single series.

Parameters:

data (ForecastInputDataset) – Input dataset whose target history provides the model context.
data

Returns:

Probabilistic forecast for the requested quantiles and horizon.

Return type:

ForecastDataset

predict_batch(data: list[ForecastInputDataset]) → BatchResult[ForecastDataset][source]

Forecast a batch of series in a single backend call.

Parameters:

data (list[ForecastInputDataset]) – Input datasets to forecast. Each provides its own target history and forecast start.
data

Returns:

One forecast per input dataset, in the same order.

Return type:

GenericAlias[ForecastDataset]

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'protected_namespaces': ()}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].