Regression

The aeon.regression module contains algorithms and composition tools for time series regression.

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

Convolution-based

HydraRegressor([n_kernels, n_groups, ...])

Hydra Regressor.

MultiRocketHydraRegressor([n_kernels, ...])

MultiRocket-Hydra Regressor.

RocketRegressor([n_kernels, estimator, ...])

Rocket transformer using RidgeCV regressor.

MiniRocketRegressor([n_kernels, ...])

MiniRocket transformer using RidgeCV regressor.

MultiRocketRegressor([n_kernels, ...])

MultiRocket transformer using RidgeCV regressor.

Deep learning

TimeCNNRegressor([n_layers, kernel_size, ...])

Time Series Convolutional Neural Network (CNN).

EncoderRegressor([n_epochs, batch_size, ...])

Establishing the network structure for an Encoder.

FCNRegressor([n_layers, n_filters, ...])

Fully Convolutional Network (FCN).

InceptionTimeRegressor([n_regressors, ...])

InceptionTime ensemble regressor.

IndividualLITERegressor([use_litemv, ...])

Single LITE or LITEMV Regressor.

IndividualInceptionRegressor([n_filters, ...])

Single Inception regressor.

LITETimeRegressor([n_regressors, ...])

LITETime or LITEMVTime ensemble Regressor.

ResNetRegressor([n_residual_blocks, ...])

Residual Neural Network.

MLPRegressor([n_layers, n_units, ...])

Multi Layer Perceptron Network (MLP).

DisjointCNNRegressor([n_layers, n_filters, ...])

Disjoint Convolutional Neural Netowkr regressor.

Distance-based

KNeighborsTimeSeriesRegressor([distance, ...])

K-Nearest Neighbour Time Series Regressor.

Feature-based

Catch22Regressor([features, catch24, ...])

Canonical Time-series Characteristics (catch22) regressor.

FreshPRINCERegressor([...])

Fresh Pipeline with RotatIoN forest Regressor.

SummaryRegressor([summary_stats, estimator, ...])

Summary statistic regressor.

TSFreshRegressor([default_fc_parameters, ...])

Time Series Feature Extraction based on Scalable Hypothesis Tests regressor.

Hybrid

RISTRegressor([n_intervals, n_shapelets, ...])

Randomised Interval-Shapelet Transformation (RIST) pipeline regressor.

Interval-based

CanonicalIntervalForestRegressor([...])

Canonical Interval Forest (CIF) Regressor.

DrCIFRegressor([base_estimator, ...])

Diverse Representation Canonical Interval Forest (DrCIF) Regressor.

IntervalForestRegressor([base_estimator, ...])

Configurable interval extracting forest regressor.

RandomIntervalRegressor([n_intervals, ...])

Random Interval Regressor.

RandomIntervalSpectralEnsembleRegressor([...])

Random Interval Spectral Ensemble (RISE) regressor.

TimeSeriesForestRegressor([base_estimator, ...])

Time series forest (TSF) regressor.

QUANTRegressor([interval_depth, ...])

QUANT interval regressor.

Shapelet-based

RDSTRegressor([max_shapelets, ...])

A random dilated shapelet transform (RDST) regressor.

sklearn

RotationForestRegressor([n_estimators, ...])

A Rotation Forest (RotF) vector regressor.

SklearnRegressorWrapper(regressor[, ...])

Wrapper for scikit-learn regressors to use the aeon framework.

Compose

RegressorEnsemble(regressors[, weights, cv, ...])

Weighted ensemble of regressors with fittable ensemble weight.

RegressorPipeline(transformers, regressor[, ...])

Pipeline of transformers and a regressor.

Dummy

DummyRegressor([strategy, constant, quantile])

DummyRegressor makes predictions that ignore the input features.

Base

BaseRegressor()

Abstract base class for time series regressors.

BaseDeepRegressor([batch_size, last_file_name])

Abstract base class for deep learning time series regression.