Time Series Regression

Selected aeon 1.6 regressors predict one continuous target per series. This is distinct from forecasting future timepoints.

Convolution-Based Regressors

Apply convolutional kernels for feature extraction:

• HydraRegressor - Multi-resolution dilated convolutions
• RocketRegressor - Random convolutional kernels
• MiniRocketRegressor - Simplified ROCKET for speed
• MultiRocketRegressor - Convolutional features from raw and differenced series with multiple pooling operators
• MultiRocketHydraRegressor - Merges ROCKET and Hydra approaches

Use when: Need fast regression with strong baseline performance.

Deep Learning Regressors

Neural architectures for end-to-end temporal regression:

• FCNRegressor - Fully convolutional network
• ResNetRegressor - Residual blocks with skip connections
• InceptionTimeRegressor - Multi-scale inception modules
• TimeCNNRegressor - Standard CNN architecture
• RecurrentRegressor - RNN/LSTM/GRU variants
• MLPRegressor - Multi-layer perceptron
• EncoderRegressor - Convolutional encoder architecture
• LITETimeRegressor - Lightweight inception time ensemble
• DisjointCNNRegressor - Specialized CNN architecture

Use when: Large datasets, complex patterns, or need feature learning.

Distance-Based Regressors

k-nearest neighbors with temporal distance metrics:

• KNeighborsTimeSeriesRegressor - k-NN with DTW, LCSS, ERP, or other distances

Use when: Small datasets, local similarity patterns, or interpretable predictions.

Feature-Based Regressors

Extract statistical features before regression:

• Catch22Regressor - 22 canonical time-series characteristics
• FreshPRINCERegressor - TSFresh features followed by rotation forest
• SummaryRegressor - Summary statistics features
• TSFreshRegressor - Automated tsfresh feature extraction

Use when: Need interpretable features or domain-specific feature engineering.

Hybrid Regressors

Combine multiple approaches:

• RISTRegressor - Randomized Interval-Shapelet Transformation

Use when: Benefit from combining interval and shapelet methods.

Interval-Based Regressors

Extract features from time intervals:

• CanonicalIntervalForestRegressor - Random intervals with decision trees
• DrCIFRegressor - Diverse Representation CIF
• TimeSeriesForestRegressor - Random interval ensemble
• RandomIntervalRegressor - Simple interval-based approach
• RandomIntervalSpectralEnsembleRegressor - Spectral interval features
• QUANTRegressor - Quantile-based interval features

Use when: Predictive patterns occur in specific time windows.

Shapelet-Based Regressors

Use discriminative subsequences for prediction:

• RDSTRegressor - Random Dilated Shapelet Transform

Use when: Need phase-invariant discriminative patterns.

Composition Tools

Build custom regression pipelines:

• RegressorPipeline - Chain transformers with regressors
• RegressorEnsemble - Weighted ensemble with learnable weights
• SklearnRegressorWrapper - Adapt sklearn regressors for time series

Utilities

• DummyRegressor - Baseline strategies (mean, median)
• BaseRegressor - Abstract base for custom regressors
• BaseDeepRegressor - Base for deep learning regressors

Quick Start

from aeon.regression.convolution_based import RocketRegressor
from aeon.datasets import load_regression

# Load data
X_train, y_train = load_regression("Covid3Month", split="train")
X_test, y_test = load_regression("Covid3Month", split="test")

# Train and predict
reg = RocketRegressor()
reg.fit(X_train, y_train)
predictions = reg.predict(X_test)

Algorithm Selection

• Speed priority: MiniRocketRegressor
• Accuracy priority: InceptionTimeRegressor, MultiRocketHydraRegressor
• Interpretability: Catch22Regressor, SummaryRegressor
• Small data: KNeighborsTimeSeriesRegressor
• Large data: Deep learning regressors, ROCKET variants
• Interval patterns: DrCIFRegressor, CanonicalIntervalForestRegressor