LagLlamaForecaster

class LagLlamaForecaster(ckpt_path=None, device=None, context_length=32, num_samples=100, batch_size=1, use_rope_scaling=False, nonnegative_pred_samples=False, use_source_package=False, validation_split=0.2, trainer_kwargs=None, lr=0.0005, aug_prob=0.0)[source]

LagLlama Foundation Model for Time Series Forecasting.

LagLlama is a foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture. This implementation supports both zero-shot prediction using pretrained weights and fine-tuning on custom data.

The model checkpoint is automatically downloaded on first use if not provided.

Usage Workflows

This model supports two main workflows:

Zero-shot (default): Uses pretrained model as-is without training. Fast inference with no training overhead. Suitable for quick predictions. Simply call fit(y) then predict().
Fine-tuning via pretrain(): Fine-tune the model on panel/hierarchical data using the pretrain() method, then call fit(y) on a specific series to set context for prediction. This is the recommended workflow for domain-specific fine-tuning. Controlled by trainer_kwargs, lr, and aug_prob parameters.

Parameters:

ckpt_pathstr, optional (default=None): Path to LagLlama checkpoint file. If None, automatically downloads from HuggingFace: “time-series-foundation-models/Lag-Llama”.
devicestr, optional (default=None): Device for inference (“cpu”, “cuda”, “cuda:0”, etc.). If None, uses CUDA if available, otherwise CPU.
context_lengthint, optional (default=32): Number of past time steps used as context for prediction. LagLlama was trained with context_length=32.
num_samplesint, optional (default=100): Number of sample paths for probabilistic forecasting.
batch_sizeint, optional (default=1): Batch size for prediction.
use_rope_scalingbool, optional (default=False): Whether to use RoPE scaling for handling longer context lengths.
nonnegative_pred_samplesbool, optional (default=False): If True, ensures all predicted samples are passed through ReLU.
use_source_packagebool, optional (default=False): If True, uses the external lag-llama package instead of vendored version.
validation_splitfloat, optional (default=0.2): Fraction of data for validation during pretrain(). Set to None to skip validation.
trainer_kwargsdict, optional (default=None): Arguments passed to PyTorch Lightning Trainer during pretrain() (e.g., {"max_epochs": 10}). If None, defaults to {"max_epochs": 50}.
lrfloat, optional (default=5e-4): Learning rate for fine-tuning during pretrain().
aug_probfloat, optional (default=0.0): Data augmentation probability during pretrain().

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]

Rasul, Kashif, et al. “Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting.” arXiv preprint arXiv:2310.08278 (2023).

Examples

Zero-shot forecasting (default)

>>> from sktime.forecasting.lagllama import LagLlamaForecaster
>>> from sktime.forecasting.base import ForecastingHorizon
>>> from sktime.datasets import load_airline
>>>
>>> y = load_airline()
>>> forecaster = LagLlamaForecaster(
...     context_length=32,
...     num_samples=100
... )
>>> fh = ForecastingHorizon([1, 2, 3, 4, 5, 6])
>>> forecaster.fit(y, fh=fh)
LagLlamaForecaster(...)
>>> y_pred = forecaster.predict()  # Point predictions
>>> # 90% prediction intervals
>>> y_interval = forecaster.predict_interval(coverage=0.9)

Fine-tuning with pretrain() on panel data

>>> from sktime.forecasting.lagllama import LagLlamaForecaster
>>> from sktime.datasets import load_airline
>>> from sktime.utils._testing.hierarchical import (
...     _make_hierarchical,
... )
>>>
>>> # Create panel data for pretraining
>>> y_panel = _make_hierarchical(
...     hierarchy_levels=(3,), min_timepoints=50, max_timepoints=50
... )
>>> # Fine-tune on panel data
>>> forecaster = LagLlamaForecaster(
...     context_length=32,
...     num_samples=100,
...     trainer_kwargs={"max_epochs": 5},
...     lr=5e-4,
...     validation_split=0.2
... )
>>> forecaster.pretrain(y_panel)  # Fine-tune on panel
LagLlamaForecaster(...)
>>> # Now fit to specific series and predict
>>> y = load_airline()
>>> forecaster.fit(y, fh=[1, 2, 3, 4, 5, 6])
LagLlamaForecaster(...)
>>> y_pred = forecaster.predict()

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`check_range_index`(df)	Check if the index is a range index.
`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.
`handle_range_index`(index)	Convert RangeIndex to Dummy DatetimeIndex.
`infer_freq`(index)	Infer frequency of the index.
`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.
`return_time_index`(df)	Return the time index, given any type of index.
`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.