SundialForecaster

Quickstart

from sktime.forecasting.sundial import SundialForecaster

estimator = SundialForecaster(model_path='thuml/sundial-base-128m', config=None, device='cpu', dtype=None, forward_kwargs=None, random_state=None, validation_split=0.2, training_args=None, compute_metrics=None, callbacks=None)

Parameters(10)

model_pathstr, default=”thuml/sundial-base-128m”

Hugging Face repository identifier or local path to a Sundial checkpoint. If None, a model is created from config with random weights and should be pretrained before it is used for meaningful forecasting.

configSundialConfig or dict, optional (default=None)

Model configuration used to initialize or override the Sundial model. If provided as a dict, it is converted with SundialConfig.from_dict. If model_path=None, the model is initialized from this config with random weights. If model_path is a checkpoint and config changes parameter shapes, incompatible checkpoint weights are initialized from scratch; pretraining or fine-tuning is recommended before forecasting.

devicestr, int, or torch.device, default=”cpu”

Device on which to place the model, for example "cpu", "cuda", or "cuda:0".

dtypetorch.dtype or str, optional (default=None)

Data type used for model loading, following the transformers dtype convention, for example torch.float16, torch.bfloat16, or "auto".

forward_kwargsdict, optional (default=None)

Additional keyword arguments forwarded to model.generate(...) during prediction. Sundial-specific options include arguments such as num_samples and revin; standard generation options supported by transformers.GenerationMixin.generate may also be passed. See the Sundial model card [3] and Transformers generation docs [4] for details.

random_stateint, RandomState instance or None, default=None

Random seed used for Sundial sampling during prediction. If set, repeated predictions from the same fitted state are reproducible.

validation_splitfloat or None, default=0.2

Fraction of data reserved for evaluation when pretrain is used. If None, no evaluation dataset is created.

training_argsdict, optional (default=None)

Keyword arguments used to construct transformers.TrainingArguments in pretrain [5].

compute_metricscallable or dict, optional (default=None)

Metrics callback(s) passed to transformers.Trainer [5].

callbackslist, optional (default=None)

Trainer callbacks passed to transformers.Trainer [5].

Examples

Simple zero-shot forecasting with thuml/sundial-base-128m:
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.sundial import SundialForecaster
>>> y = load_airline ()
>>> forecaster = SundialForecaster ()
>>> forecaster. fit (y)
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ]) Running with explicit device, dtype, and sampling settings:
>>> import torch
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.sundial import SundialForecaster
>>> y = load_airline ()
>>> forecaster = SundialForecaster (
... device = "cuda",
... dtype = torch. bfloat16,
... forward_kwargs = { "num_samples": 20 },
... random_state = 42,
... )
>>> y_pred = forecaster. fit (y). predict (fh = [1, 2, 3 ]) Passing Sundial and Transformers generation options through forward_kwargs:
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.sundial import SundialForecaster
>>> y = load_airline ()
>>> forecaster = SundialForecaster (
... forward_kwargs = { "num_samples": 20, "revin": False },
... )
>>> y_pred = forecaster. fit (y). predict (fh = [1, 2, 3 ]) Quantile prediction from generated samples:
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.sundial import SundialForecaster
>>> y = load_airline ()
>>> forecaster = SundialForecaster (
... forward_kwargs = { "num_samples": 50 },
... )
>>> forecaster. fit (y)
>>> y_pred = forecaster. predict_quantiles (
... fh = [1, 2, 3 ],
... alpha = [0.1, 0.5, 0.9 ],
... ) Global training on panel data before forecasting a single series: The example below changes output_token_lens. This can initialize the affected prediction-head weights from scratch, so pretrain is run before forecasting.
>>> import torch
>>> from sktime.datasets import load_airline, load_tecator
>>> from sktime.forecasting.sundial import SundialForecaster
>>> device = "cuda" if torch. cuda. is_available () else None
>>> y_panel = load_tecator (
... return_type = "pd-multiindex",
... return_X_y = False,
... )
>>> y_panel = y_panel. drop (["class_val" ], axis = 1)
>>> y = load_airline ()
>>> forecaster = SundialForecaster (
... training_args = {
... "output_dir": "sundial-output",
... "do_train": True,
... "do_eval": True,
... "evaluation_strategy": "epoch",
... "num_train_epochs": 10,
... "per_device_train_batch_size": 8,
... "per_device_eval_batch_size": 8,
... "learning_rate": 5e-5,
... },
... config = { "output_token_lens": [8 ]},
... device = device,
... dtype = torch. bfloat16,
... validation_split = 0.3,
... )
>>> forecaster. pretrain (y_panel)
>>> forecaster. fit (y)
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

References