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skills/aeon/references/regression.md

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+# Time Series Regression
+
+Aeon provides time series regressors across 9 categories for predicting continuous values from temporal sequences.
+
+## Convolution-Based Regressors
+
+Apply convolutional kernels for feature extraction:
+
+- `HydraRegressor` - Multi-resolution dilated convolutions
+- `RocketRegressor` - Random convolutional kernels
+- `MiniRocketRegressor` - Simplified ROCKET for speed
+- `MultiRocketRegressor` - Combined ROCKET variants
+- `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` - Generic encoder wrapper
+- `LITERegressor` - 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` - Pipeline combining multiple feature extractors
+- `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
+
+```python
+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