CiscoTSMForecaster

class CiscoTSMForecaster(model_path: str = 'cisco-ai/cisco-time-series-model-1.0', num_layers: int = 25, backend: str = 'cpu', context_length=None, quantiles=None, ignore_deps: bool = False)[source]

Zero-shot univariate forecaster using the Cisco Time Series Model (CTSM).

CTSM 1.0 is a 250M-parameter, decoder-only transformer foundation model developed by Cisco (Splunk) for univariate zero-shot time series forecasting [1]. It uses a multiresolution architecture: internally it derives a coarse-resolution context (60 sparser than the input) and a fine-resolution context from a single time series, then predicts up to 128 steps ahead. Long-horizon forecasting beyond 128 steps is supported via autoregressive rolling.

Calling fit only stores the training context and loads the model weights. No model training or fine-tuning is performed.

Parameters:

model_pathstr, default=”cisco-ai/cisco-time-series-model-1.0”

HuggingFace repository ID for the CTSM checkpoint. Use "cisco-ai/cisco-time-series-model-1.0-preview" for the earlier, larger 500M-parameter preview checkpoint (requires num_layers=50).

num_layersint, default=25

Number of transformer layers. Use 25 for CTSM 1.0 and 50 for 1.0-preview.

backendstr, default=”cpu”

Hardware backend: "cpu" or "gpu". When set to "gpu", the model is placed on the first available CUDA device. If no GPU is found, the package falls back to CPU automatically.

context_lengthint or None, default=None

Maximum number of most-recent observations to pass as context. If None, the full training series (up to the model’s internal maximum of 30 720 points) is used. Shorter contexts reduce memory usage but may degrade forecast quality.

quantileslist of float or None, default=None

Quantile levels pre-computed by the model. If None, the official 15-quantile set is used: ``[0.01, 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,

0.6, 0.7, 0.75, 0.8, 0.9, 0.95, 0.99]``.

These levels are available via predict_quantiles / predict_interval. Arbitrary alpha values not in this set are handled by linear interpolation over the available levels.

ignore_depsbool, default=False

If True, soft-dependency checks for cisco-tsm and torch are skipped. Useful for testing the sktime adapter contract without the optional packages installed.

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.

Notes

CTSM is a univariate model. Multivariate targets are not supported.
Exogenous variables are not supported.
In-sample prediction is not supported.
The loaded CiscoTsmMR object is cached in-process via a multiton keyed on (model_path, num_layers, backend) to avoid redundant model loading across multiple estimator instances.
The model is excluded from the pickle state to keep serialization lightweight; it is reloaded transparently on the first predict call after unpickling.

References

[1]

Liang Gou et al. “Cisco Time Series Model Technical Report.” arXiv:2511.19841, 2025. https://arxiv.org/abs/2511.19841

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.cisco_tsm import CiscoTSMForecaster
>>> y = load_airline()
>>> forecaster = CiscoTSMForecaster()
>>> forecaster.fit(y)
CiscoTSMForecaster(...)
>>> y_pred = forecaster.predict(fh=[1, 2, 3])
>>> pred_int = forecaster.predict_interval(fh=[1, 2, 3],coverage=0.9)

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.