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Transformations

Aeon provides extensive transformation capabilities for preprocessing, feature extraction, and representation learning from time series data.

Transformation Types

Aeon distinguishes between: - CollectionTransformers: Transform multiple time series (collections) - SeriesTransformers: Transform individual time series

Collection Transformers

Convolution-Based Feature Extraction

Fast, scalable feature generation using random kernels:

• RocketTransformer - Random convolutional kernels
• MiniRocketTransformer - Simplified ROCKET for speed
• MultiRocketTransformer - Enhanced ROCKET variant
• HydraTransformer - Multi-resolution dilated convolutions
• MultiRocketHydraTransformer - Combines ROCKET and Hydra
• ROCKETGPU - GPU-accelerated variant

Use when: Need fast, scalable features for any ML algorithm, strong baseline performance.

Statistical Feature Extraction

Domain-agnostic features based on time series characteristics:

• Catch22 - 22 canonical time-series characteristics
• TSFresh - Comprehensive automated feature extraction (100+ features)
• TSFreshRelevant - Feature extraction with relevance filtering
• SevenNumberSummary - Descriptive statistics (mean, std, quantiles)

Use when: Need interpretable features, domain-agnostic approach, or feeding traditional ML.

Dictionary-Based Representations

Symbolic approximations for discrete representations:

• SAX - Symbolic Aggregate approXimation
• PAA - Piecewise Aggregate Approximation
• SFA - Symbolic Fourier Approximation
• SFAFast - Optimized SFA
• SFAWhole - SFA on entire series (no windowing)
• BORF - Bag-of-Receptive-Fields

Use when: Need discrete/symbolic representation, dimensionality reduction, interpretability.

Shapelet-Based Features

Discriminative subsequence extraction:

• RandomShapeletTransform - Random discriminative shapelets
• RandomDilatedShapeletTransform - Dilated shapelets for multi-scale
• SAST - Scalable And Accurate Subsequence Transform
• RSAST - Randomized SAST

Use when: Need interpretable discriminative patterns, phase-invariant features.

Interval-Based Features

Statistical summaries from time intervals:

• RandomIntervals - Features from random intervals
• SupervisedIntervals - Supervised interval selection
• QUANTTransformer - Quantile-based interval features

Use when: Predictive patterns localized to specific windows.

Preprocessing Transformations

Data preparation and normalization:

• MinMaxScaler - Scale to [0, 1] range
• Normalizer - Z-normalization (zero mean, unit variance)
• Centerer - Center to zero mean
• SimpleImputer - Fill missing values
• DownsampleTransformer - Reduce temporal resolution
• Tabularizer - Convert time series to tabular format

Use when: Need standardization, missing value handling, format conversion.

Specialized Transformations

Advanced analysis methods:

• MatrixProfile - Computes distance profiles for pattern discovery
• DWTTransformer - Discrete Wavelet Transform
• AutocorrelationFunctionTransformer - ACF computation
• Dobin - Distance-based Outlier BasIs using Neighbors
• SignatureTransformer - Path signature methods
• PLATransformer - Piecewise Linear Approximation

Class Imbalance Handling

• ADASYN - Adaptive Synthetic Sampling
• SMOTE - Synthetic Minority Over-sampling
• OHIT - Over-sampling with Highly Imbalanced Time series

Use when: Classification with imbalanced classes.

Pipeline Composition

• CollectionTransformerPipeline - Chain multiple transformers

Series Transformers

Transform individual time series (e.g., for preprocessing in forecasting).

Statistical Analysis

• AutoCorrelationSeriesTransformer - Autocorrelation
• StatsModelsACF - ACF using statsmodels
• StatsModelsPACF - Partial autocorrelation

Smoothing and Filtering

• ExponentialSmoothing - Exponentially weighted moving average
• MovingAverage - Simple or weighted moving average
• SavitzkyGolayFilter - Polynomial smoothing
• GaussianFilter - Gaussian kernel smoothing
• BKFilter - Baxter-King bandpass filter
• DiscreteFourierApproximation - Fourier-based filtering

Use when: Need noise reduction, trend extraction, or frequency filtering.

Dimensionality Reduction

• PCASeriesTransformer - Principal component analysis
• PlASeriesTransformer - Piecewise Linear Approximation

Transformations

• BoxCoxTransformer - Variance stabilization
• LogTransformer - Logarithmic scaling
• ClaSPTransformer - Classification Score Profile

Pipeline Composition

• SeriesTransformerPipeline - Chain series transformers

Quick Start: Feature Extraction

from aeon.transformations.collection.convolution_based import RocketTransformer
from aeon.classification.sklearn import RotationForest
from aeon.datasets import load_classification

# Load data
X_train, y_train = load_classification("GunPoint", split="train")
X_test, y_test = load_classification("GunPoint", split="test")

# Extract ROCKET features
rocket = RocketTransformer()
X_train_features = rocket.fit_transform(X_train)
X_test_features = rocket.transform(X_test)

# Use with any sklearn classifier
clf = RotationForest()
clf.fit(X_train_features, y_train)
accuracy = clf.score(X_test_features, y_test)

Quick Start: Preprocessing Pipeline

from aeon.transformations.collection import (
    MinMaxScaler,
    SimpleImputer,
    CollectionTransformerPipeline
)

# Build preprocessing pipeline
pipeline = CollectionTransformerPipeline([
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', MinMaxScaler())
])

X_transformed = pipeline.fit_transform(X_train)

Quick Start: Series Smoothing

from aeon.transformations.series import MovingAverage

# Smooth individual time series
smoother = MovingAverage(window_size=5)
y_smoothed = smoother.fit_transform(y)

Algorithm Selection

For Feature Extraction:

• Speed + Performance: MiniRocketTransformer
• Interpretability: Catch22, TSFresh
• Dimensionality reduction: PAA, SAX, PCA
• Discriminative patterns: Shapelet transforms
• Comprehensive features: TSFresh (with longer runtime)

For Preprocessing:

• Normalization: Normalizer, MinMaxScaler
• Smoothing: MovingAverage, SavitzkyGolayFilter
• Missing values: SimpleImputer
• Frequency analysis: DWTTransformer, Fourier methods

For Symbolic Representation:

• Fast approximation: PAA
• Alphabet-based: SAX
• Frequency-based: SFA, SFAFast

Best Practices

1. Fit on training data only: Avoid data leakage python transformer.fit(X_train) X_train_tf = transformer.transform(X_train) X_test_tf = transformer.transform(X_test)

2. Pipeline composition: Chain transformers for complex workflows python pipeline = CollectionTransformerPipeline([ ('imputer', SimpleImputer()), ('scaler', Normalizer()), ('features', RocketTransformer()) ])

3. Feature selection: TSFresh can generate many features; consider selection python from sklearn.feature_selection import SelectKBest selector = SelectKBest(k=100) X_selected = selector.fit_transform(X_features, y)

4. Memory considerations: Some transformers memory-intensive on large datasets

5. Use MiniRocket instead of ROCKET for speed
6. Consider downsampling for very long series

7. Use ROCKETGPU for GPU acceleration

8. Domain knowledge: Choose transformations matching domain:

9. Periodic data: Fourier-based methods
10. Noisy data: Smoothing filters
11. Spike detection: Wavelet transforms