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Forecaster

FalconXForecaster

API referenceSource
Categorical in XPred intMissing valuesMultivariate

Falcon-X forecaster — zero-shot via remote HTTP API.

This forecaster wraps the Falcon-X multivariate time series foundation model [1], [2] released by Ant International in June 2026. Falcon-X is a closed-source model; it is accessed through plain HTTP POST requests to Ant International’s hosted inference endpoint — no model weights are downloaded or stored locally, and sktime does not ship or depend on any Falcon-X client library.

The primary workflow is fit + predict for zero-shot inference. fit stores the observed series as forecasting context. predict calls the Falcon-X API and returns point forecasts. predict_quantiles / predict_interval are also supported because the API natively returns 21 probability quantiles.

Model training and fine-tuning are not supported.

Quickstart

python
from sktime.forecasting.falcon_x import FalconXForecaster

estimator = FalconXForecaster(context_length=None, quantile_level=0.5, license_accepted=False, endpoint=None, timeout=30.0)

Parameters(5)

context_lengthint or None, default=None

Number of most-recent time steps to pass as context to the model. If None, all available history is used.

quantile_levelfloat, default=0.5

The quantile level used for point forecasts returned by predict. Must be one of the 21 supported levels: [0.01, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 0.99]. Defaults to 0.5 (median).

license_acceptedbool, default=False

Falcon-X is a closed-source model made available via a remote proprietary API operated by Ant International. Usage is subject to Ant International’s licence and API terms of service, which differ from sktime’s BSD-3-Clause licence.

You must set license_accepted=True to confirm that you have read and accepted the Falcon-X licence and API terms before using this forecaster. Leaving this as False (the default) will raise a ValueError at construction time.

endpointstr or None, default=None

Custom API endpoint URL. If None, the default Falcon Studio endpoint is used. If "mock", a built-in mock that returns random predictions is used instead of the real API — useful for tests or local experimentation without network access.

timeoutfloat, default=30.0
Request timeout in seconds for the API call.

Examples

Zero-shot univariate point forecasting:
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.falcon_x import FalconXForecaster
>>> y = load_airline ()
>>> forecaster = FalconXForecaster (license_accepted = True)
>>> forecaster. fit (y) FalconXForecaster(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ]) Probabilistic/quantile forecasting:
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.falcon_x import FalconXForecaster
>>> y = load_airline ()
>>> forecaster = FalconXForecaster (license_accepted = True)
>>> forecaster. fit (y) FalconXForecaster(
... )
>>> y_pred_q = forecaster. predict_quantiles (
... fh = [1, 2, 3 ], alpha = [0.1, 0.5, 0.9 ]
... ) Mock mode for offline testing or local experimentation (random predictions, no network access required):
>>> from sktime.forecasting.falcon_x import FalconXForecaster
>>> forecaster = FalconXForecaster (
... endpoint = "mock", license_accepted = True
... ) Multivariate forecasting with a shorter context window:
>>> import pandas as pd
>>> import numpy as np
>>> from sktime.forecasting.falcon_x import FalconXForecaster
>>> n, c = 100, 3
>>> y = pd. DataFrame (
... np. random. randn (n, c),
... index = pd. date_range ("2020", periods = n, freq = "ME"),
... )
>>> forecaster = FalconXForecaster (
... context_length = 64, license_accepted = True
... )
>>> forecaster. fit (y) FalconXForecaster(context_length=64)
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

References

[1]

Falcon-TST repository: https://github.com/ant-intl/Falcon-TST