Transformations

The aeon.transformations module contains classes for series transformations. The module is organised into CollectionTransformers which transform a collection of time series into a different representation and SeriesTransformers which transform single time series.

All transformers in aeon can be listed using the aeon.registry .all_estimators utility, using estimator_types=”transformer” tag.

Collection transformers

BaseCollectionTransformer()

Transformer base class for collections.

AutocorrelationFunctionTransformer([n_lags, ...])

Autocorrelation function transformer.

ARCoefficientTransformer([order, ...])

Autoreggression coefficient feature transformer.

DownsampleTransformer([downsample_by, ...])

Downsample the time dimension of a collection of time series.

DWTTransformer([n_levels])

Discrete Wavelet Transform Transformer.

HOG1DTransformer([n_intervals, n_bins, ...])

HOG1D transform.

MatrixProfile([m])

Return the matrix profile and index profile for each time series of a dataset.

Normalise([method])

Normaliser transformer for collections.

Padder([pad_length, fill_value])

Pad unequal length time series to equal, fixed length.

PeriodogramTransformer([pad_series, ...])

Periodogram transformer.

Tabularizer()

A transformer that turns time series collection into tabular data.

Resizer(length)

Time series interpolator/re-sampler.

TimeSeriesScaler([copy, with_mean, with_std])

StandardScaler for time series.

SlopeTransformer([n_intervals])

Piecewise slope transformation.

Truncator([truncated_length])

Truncate unequal length time series to a lower bounds.

Channel selection

ChannelScorer(estimator[, scoring_function, ...])

Performs channel selection using a single channel classifier or regressor.

ElbowClassPairwise([distance, ...])

Elbow Class Pairwise (ECP) transformer to select a subset of channels.

ElbowClassSum([distance, prototype_type, ...])

Elbow Class Sum (ECS) transformer to select a subset of channels/variables.

RandomChannelSelector([p, random_state])

Selects a random proportion of channels.

Compose

CollectionTransformerPipeline(transformers)

Pipeline of collection transformers.

Convolution based

Rocket([num_kernels, normalise, n_jobs, ...])

RandOm Convolutional KErnel Transform (ROCKET).

MiniRocket([num_kernels, ...])

MINImally RandOm Convolutional KErnel Transform (MiniRocket).

MiniRocketMultivariateVariable([...])

MINIROCKET (Multivariate, unequal length).

MultiRocket([num_kernels, ...])

Multi RandOm Convolutional KErnel Transform (MultiRocket).

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

Hydra Transformer.

Dictionary-based features

SAX([n_segments, alphabet_size, ...])

Symbolic Aggregate approXimation (SAX) transformer.

PAA([n_segments])

Piecewise Aggregate Approximation Transformer (PAA).

SFA([word_length, alphabet_size, ...])

Symbolic Fourier Approximation (SFA) Transformer.

SFAFast([word_length, alphabet_size, ...])

Symbolic Fourier Approximation (SFA) Transformer.

BORF([window_size_min_window_size, ...])

Bag-of-Receptive-Fields (BORF) Transformer.

Feature based

Catch22([features, catch24, outlier_norm, ...])

Canonical Time-series Characteristics (Catch22).

TSFresh([default_fc_parameters, ...])

Transformer for extracting time series features via tsfresh.extract_features.

TSFreshRelevant([default_fc_parameters, ...])

Transformer for extracting time series features via tsfresh.extract_features.

SevenNumberSummary([summary_stats])

Seven-number summary transformer.

Interval based

RandomIntervals([n_intervals, ...])

Random interval feature transformer.

SupervisedIntervals([n_intervals, ...])

Supervised interval feature transformer.

QUANTTransformer([interval_depth, ...])

QUANT interval transform.

Shapelet based

RandomShapeletTransform([...])

Random Shapelet Transform.

RandomDilatedShapeletTransform([...])

Random Dilated Shapelet Transform (RDST) as described in [R1a26faa97573-1], [R1a26faa97573-2].

SAST([lengths, stride, nb_inst_per_class, ...])

Scalable and Accurate Subsequence Transform (SAST).

RSAST([n_random_points, len_method, ...])

Random Scalable and Accurate Subsequence Transform (RSAST).

Signature based

SignatureTransformer([augmentation_list, ...])

Transformation class from the signature method.

Series transforms

BaseSeriesTransformer(axis)

Transformer base class for collections.

AutoCorrelationSeriesTransformer([n_lags])

Auto-correlation transformer.

ClaSPTransformer([window_length, ...])

ClaSP (Classification Score Profile) Transformer.

DFTSeriesTransformer([r, sort])

Filter a times series using Discrete Fourier Approximation (DFT).

Dobin([frac, k])

Distance based Outlier BasIs using Neighbors (DOBIN).

GaussSeriesTransformer([sigma, order])

Filter a times series using Gaussian filter.

MatrixProfileSeriesTransformer([window_length])

Calculate the matrix profile of a time series.

PLASeriesTransformer([max_error, ...])

Piecewise Linear Approximation (PLA) for time series transformation.

SGSeriesTransformer([window_length, polyorder])

Filter a times series using Savitzky-Golay (SG).

StatsModelsACF([adjusted, n_lags, fft, missing])

Auto-correlation wrapper for statsmodels.

StatsModelsPACF([n_lags, method])

Partial auto-correlation wrapper for statsmodels.

BKFilter([low, high, K])

Filter a times series using the Baxter-King filter.

BoxCoxTransformer([bounds, method, sp])

Box-Cox power transform.

Dobin([frac, k])

Distance based Outlier BasIs using Neighbors (DOBIN).

ScaledLogitSeriesTransformer([lower_bound, ...])

Scaled logit transform or Log transform.

SIVSeriesTransformer([window_length])

Filter a times series using Recursive Median Sieve (SIV).

PCASeriesTransformer([n_components, copy, ...])

Principal Components Analysis applied as transformer.

WarpingSeriesTransformer([series_index, ...])

Warping Path Transformer.