TimerForecaster

Timer foundation model forecaster.

Quickstart

from sktime.forecasting.timer import TimerForecaster

estimator = TimerForecaster(model_name='thuml/timer-base-84m', context_length=2880, device='cpu')

Parameters(3)

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.

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 ])

References

1. [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.