TotoForecaster
Toto foundation model forecaster for zero-shot forecasting.
Direct interface to forecaster from DataDog/toto [1].
Toto is a foundation model for multivariate time series forecasting with a focus on observability metrics. This model leverages innovative architectural designs to efficiently handle the high-dimensional, complex time series that are characteristic of observability data. Generate both point forecasts and uncertainty estimates using a Student-T mixture model. Support for variable prediction horizons and context lengths.
Known-future exogenous variables X are supported via Toto’s native exogenous mechanism: the columns of X are appended after the target channels as exogenous variates. When X is used, it must be supplied for every step of the forecast horizon, i.e. for all steps 1 .. max(fh) ahead of the cutoff (no gaps), since Toto consumes the known future values at each autoregressive step.
Schnellstart
from sktime.forecasting.toto import TotoForecaster
estimator = TotoForecaster(seed=None, num_samples: int=1, samples_per_batch: int=1, prediction_type: str='median', scale_factor_exponent: int=10, stabilize_with_global: bool=True, use_memory_efficient_attention: bool=False, model_path: str='Datadog/Toto-Open-Base-1.0', device=None)Parameter(8)
- num_samplesint
- Number of samples for probabilistic forecasting
- samples_per_batchint, optional (default=1)
- Control memory usage during inference
- prediction_typestring, optional (default=’median’)
- Type of prediction to generate (‘mean’ or ‘median’).
- scale_factor_exponentint, optional (default=10)
- Exponent for the scale factor used in the model.
- stabilize_with_globalboolean, optional (default=True)
- Whether to stabilize the model with global context.
- use_memory_efficient_attentionboolean, optional (default=True)
- Whether to use memory-efficient attention mechanisms using Xformers.
- model_pathstring, optional (default=’Datadog/Toto-Open-Base-1.0’)
- Path to the Toto huggingface model.
- devicestring, optional (default=None)
- Specifies the device on which to run the model on (‘cpu’ or ‘cuda’).
Beispiele
>>> from sktime.datasets import load_longley
>>> from sktime.forecasting.toto import TotoForecaster
>>> _, y = load_longley ()
>>> model = TotoForecaster ()
>>> model. fit (y) TotoForecaster()
>>> forecast = model. predict (fh = [1, 2, 5 ]) With known-future exogenous variables:
>>> from sktime.forecasting.model_selection import temporal_train_test_split
>>> X, y = load_longley ()
>>> y_train, _, X_train, X_test = temporal_train_test_split (y, X, test_size = 3)
>>> model = TotoForecaster ()
>>> model. fit (y_train, X = X_train) TotoForecaster()
>>> forecast = model. predict (fh = [1, 2, 3 ], X = X_test)