Forecasting

The aeon.forecasting module contains algorithms and composition tools for forecasting.

All clusterers in aeon``can be listed using the ``aeon.registry.all_estimators utility, using estimator_types="forecaster", optionally filtered by tags. Valid tags can be listed using aeon.registry.all_tags.

Base

Pipeline composition

Compositors for building forecasting pipelines. Pipelines can also be constructed using *, +, and | dunders.

Reduction

Reduction forecasters that use sklearn regressors or aeon time series regressors to make forecasts. Use make_reduction for easy specification.

Naive forecaster

Prediction intervals

Wrappers that add prediction intervals to any forecaster.

Trend forecasters

Exponential smoothing based forecasters

AR/MA type forecasters

Forecasters with AR or MA component. All “ARIMA” models below include SARIMAX capability.

Structural time series models

Ensembles and stacking

Hierarchical reconciliation

Online and stream forecasting

Model selection and tuning

Model Evaluation (Backtesting)

Time series splitters

Time series splitters can be used in both evaluation and tuning.