119 lines
3.9 KiB
Markdown
119 lines
3.9 KiB
Markdown
# Time Series Regression
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Aeon provides time series regressors across 9 categories for predicting continuous values from temporal sequences.
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## Convolution-Based Regressors
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Apply convolutional kernels for feature extraction:
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- `HydraRegressor` - Multi-resolution dilated convolutions
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- `RocketRegressor` - Random convolutional kernels
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- `MiniRocketRegressor` - Simplified ROCKET for speed
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- `MultiRocketRegressor` - Combined ROCKET variants
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- `MultiRocketHydraRegressor` - Merges ROCKET and Hydra approaches
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**Use when**: Need fast regression with strong baseline performance.
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## Deep Learning Regressors
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Neural architectures for end-to-end temporal regression:
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- `FCNRegressor` - Fully convolutional network
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- `ResNetRegressor` - Residual blocks with skip connections
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- `InceptionTimeRegressor` - Multi-scale inception modules
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- `TimeCNNRegressor` - Standard CNN architecture
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- `RecurrentRegressor` - RNN/LSTM/GRU variants
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- `MLPRegressor` - Multi-layer perceptron
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- `EncoderRegressor` - Generic encoder wrapper
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- `LITERegressor` - Lightweight inception time ensemble
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- `DisjointCNNRegressor` - Specialized CNN architecture
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**Use when**: Large datasets, complex patterns, or need feature learning.
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## Distance-Based Regressors
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k-nearest neighbors with temporal distance metrics:
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- `KNeighborsTimeSeriesRegressor` - k-NN with DTW, LCSS, ERP, or other distances
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**Use when**: Small datasets, local similarity patterns, or interpretable predictions.
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## Feature-Based Regressors
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Extract statistical features before regression:
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- `Catch22Regressor` - 22 canonical time-series characteristics
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- `FreshPRINCERegressor` - Pipeline combining multiple feature extractors
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- `SummaryRegressor` - Summary statistics features
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- `TSFreshRegressor` - Automated tsfresh feature extraction
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**Use when**: Need interpretable features or domain-specific feature engineering.
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## Hybrid Regressors
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Combine multiple approaches:
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- `RISTRegressor` - Randomized Interval-Shapelet Transformation
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**Use when**: Benefit from combining interval and shapelet methods.
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## Interval-Based Regressors
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Extract features from time intervals:
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- `CanonicalIntervalForestRegressor` - Random intervals with decision trees
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- `DrCIFRegressor` - Diverse Representation CIF
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- `TimeSeriesForestRegressor` - Random interval ensemble
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- `RandomIntervalRegressor` - Simple interval-based approach
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- `RandomIntervalSpectralEnsembleRegressor` - Spectral interval features
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- `QUANTRegressor` - Quantile-based interval features
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**Use when**: Predictive patterns occur in specific time windows.
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## Shapelet-Based Regressors
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Use discriminative subsequences for prediction:
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- `RDSTRegressor` - Random Dilated Shapelet Transform
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**Use when**: Need phase-invariant discriminative patterns.
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## Composition Tools
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Build custom regression pipelines:
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- `RegressorPipeline` - Chain transformers with regressors
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- `RegressorEnsemble` - Weighted ensemble with learnable weights
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- `SklearnRegressorWrapper` - Adapt sklearn regressors for time series
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## Utilities
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- `DummyRegressor` - Baseline strategies (mean, median)
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- `BaseRegressor` - Abstract base for custom regressors
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- `BaseDeepRegressor` - Base for deep learning regressors
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## Quick Start
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```python
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from aeon.regression.convolution_based import RocketRegressor
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from aeon.datasets import load_regression
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# Load data
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X_train, y_train = load_regression("Covid3Month", split="train")
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X_test, y_test = load_regression("Covid3Month", split="test")
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# Train and predict
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reg = RocketRegressor()
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reg.fit(X_train, y_train)
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predictions = reg.predict(X_test)
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```
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## Algorithm Selection
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- **Speed priority**: MiniRocketRegressor
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- **Accuracy priority**: InceptionTimeRegressor, MultiRocketHydraRegressor
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- **Interpretability**: Catch22Regressor, SummaryRegressor
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- **Small data**: KNeighborsTimeSeriesRegressor
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- **Large data**: Deep learning regressors, ROCKET variants
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- **Interval patterns**: DrCIFRegressor, CanonicalIntervalForestRegressor
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