TimerForecaster

TimerForecaster

class TimerForecaster(model_name='thuml/timer-base-84m', context_length=2880, device='cpu')[source]

Timer foundation model forecaster.

Wraps the Timer generative pre-trained Transformer for zero-shot time series forecasting via the HuggingFace transformers library.

Timer uses autoregressive generation on continuous time series tokens. The model is pre-trained on the Unified Time Series Dataset (UTSD) covering diverse domains and temporal patterns.

The model is cached using the multiton pattern to avoid reloading weights when multiple forecaster instances share the same model.

Parameters:

model_namestr, default=”thuml/timer-base-84m”

Name or path of the pre-trained Timer model on HuggingFace. Options include:

• “thuml/timer-base-84m” (84M parameters)

• “thuml/timer-xl-84m” (Timer-XL variant)

context_lengthint, default=2880

Number of historical observations to use as input context. Timer supports variable context lengths. If the series is shorter, the full series is used.

devicestr, default=”cpu”

Device to run the model on. Options: “cpu”, “cuda”, “cuda:0”, etc.

Attributes:

cutoff

Cut-off = “present time” state of forecaster.

fh

Forecasting horizon that was passed.

is_fitted

Whether fit has been called.

state

State of the estimator.

References

[1]

Liu et al., “Timer: Generative Pre-trained Transformers Are Large Time Series Models”, ICML 2024. https://arxiv.org/abs/2402.02368

[2]

Liu et al., “Timer-XL: Long-Context Transformers for Unified Time Series Forecasting”, ICLR 2025.

Examples

>>> from sktime.forecasting.timer import TimerForecaster
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> forecaster = TimerForecaster(
...     model_name="thuml/timer-base-84m",
... )
>>> forecaster.fit(y)
>>> y_pred = forecaster.predict(fh=[1, 2, 3])

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(y[, X, fh])	Fit forecaster to training data.
fit_predict(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_pretrained_params([deep])	Get pretrained parameters of this estimator.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
predict([fh, X])	Forecast time series at future horizon.
predict_interval([fh, X, coverage])	Compute/return prediction interval forecasts.
predict_proba([fh, X, marginal])	Compute/return fully probabilistic forecasts.
predict_quantiles([fh, X, alpha])	Compute/return quantile forecasts.
predict_residuals([y, X])	Return residuals of time series forecasts.
predict_var([fh, X, cov])	Compute/return variance forecasts.
pretrain(y[, X, fh])	Pre-train forecaster on panel (global) data.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
update(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
update_predict(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
update_predict_single([y, fh, X, update_params])	Update model with new data and make forecasts.