API Documentation#

Full API documentation of the pyts Python package.

pyts.approximation: Approximation algorithms#

The pyts.approximation module includes approximation algorithms.

approximation.DiscreteFourierTransform([...])

Discrete Fourier Transform.

approximation.MultipleCoefficientBinning([...])

Bin continuous data into intervals column-wise.

approximation.PiecewiseAggregateApproximation([...])

Piecewise Aggregate Approximation.

approximation.SymbolicAggregateApproximation([...])

Symbolic Aggregate approXimation.

approximation.SymbolicFourierApproximation([...])

Symbolic Fourier Approximation.

pyts.bag_of_words: Bag-of-words algorithms#

The pyts.bag_of_words module includes bag-of-words algorithms.

bag_of_words.BagOfWords([window_size, ...])

Bag-of-words representation for time series.

bag_of_words.WordExtractor([window_size, ...])

Transform discretized time series into sequences of words.

pyts.classification: Classification algorithms#

The pyts.classification module includes classification algorithms.

classification.BOSSVS([word_size, n_bins, ...])

Bag-of-SFA Symbols in Vector Space.

classification.KNeighborsClassifier([...])

k-nearest neighbors classifier.

classification.LearningShapelets([...])

Learning Shapelets algorithm.

classification.SAXVSM([window_size, ...])

Classifier based on SAX-VSM representation and tf-idf statistics.

classification.TimeSeriesForest([...])

A random forest classifier for time series.

classification.TSBF([n_estimators, ...])

Time Series Bag-of-Features algorithm.

pyts.datasets: Dataset loading utilities#

The pyts.datasets module tools for making, loading and fetching time series datasets.

datasets.fetch_ucr_dataset(dataset[, ...])

Fetch dataset from UCR TSC Archive by name.

datasets.fetch_uea_dataset(dataset[, ...])

Fetch dataset from UEA TSC Archive by name.

datasets.load_basic_motions([return_X_y])

Load and return the Basic Motions dataset.

datasets.load_coffee([return_X_y])

Load and return the Coffee dataset.

datasets.load_gunpoint([return_X_y])

Load and return the GunPoint dataset.

datasets.load_pig_central_venous_pressure([...])

Load and return the PigCVP dataset.

datasets.make_cylinder_bell_funnel([...])

Make a Cylinder-Bell-Funnel dataset.

datasets.ucr_dataset_info([dataset])

Information about the UCR datasets.

datasets.ucr_dataset_list()

List of available UCR datasets.

datasets.uea_dataset_info([dataset])

Information about the UEA datasets.

datasets.uea_dataset_list()

List of available UEA datasets.

pyts.decomposition: Decomposition algorithms#

The pyts.decomposition module includes decomposition algorithms.

decomposition.SingularSpectrumAnalysis([...])

Singular Spectrum Analysis.

pyts.image: Imaging algorithms#

The pyts.image module includes algorithms that transform times series into images.

image.GramianAngularField([image_size, ...])

Gramian Angular Field.

image.MarkovTransitionField([image_size, ...])

Markov Transition Field.

image.RecurrencePlot([dimension, ...])

Recurrence Plot.

pyts.metrics: Metrics#

The pyts.metrics module includes metrics.

metrics.boss(x, y)

Return the BOSS distance between two arrays.

metrics.dtw([x, y, dist, method, options, ...])

Dynamic Time Warping (DTW) distance between two samples.

metrics.itakura_parallelogram(n_timestamps_1)

Compute the Itakura parallelogram.

metrics.sakoe_chiba_band(n_timestamps_1[, ...])

Compute the Sakoe-Chiba band.

metrics.show_options([method, disp])

Show documentation for additional options of Dynamic Time Warping methods.

pyts.multivariate: Multivariate time series tools#

The pyts.multivariate module includes tools to deal for multivariate time series.

Classification#

multivariate.classification.MultivariateClassifier(...)

Classifier for multivariate time series.

Image#

multivariate.image.JointRecurrencePlot([...])

Joint Recurrence Plot.

Transformation#

multivariate.transformation.MultivariateTransformer(...)

Transformer for multivariate time series.

multivariate.transformation.WEASELMUSE([...])

WEASEL+MUSE algorithm.

Utils#

multivariate.utils.check_3d_array(X)

Check that the input is a three-dimensional array.

pyts.preprocessing: Preprocessing tools#

The pyts.preprocessing module includes preprocessing algorithms.

Scaling#

preprocessing.MaxAbsScaler()

Scale each sample by its maximum absolute value.

preprocessing.MinMaxScaler([sample_range])

Transforms samples by scaling each sample to a given range.

preprocessing.RobustScaler([with_centering, ...])

Scale samples using statistics that are robust to outliers.

preprocessing.StandardScaler([with_mean, ...])

Standardize time series by removing mean and scaling to unit variance.

Transformation#

preprocessing.PowerTransformer([method, ...])

Apply a power transform sample-wise to make data more Gaussian-like.

preprocessing.QuantileTransformer([...])

Transform samples using quantiles information.

Discretizing#

preprocessing.KBinsDiscretizer([n_bins, ...])

Bin continuous data into intervals sample-wise.

Imputation#

preprocessing.InterpolationImputer([...])

Impute missing values using interpolation.

pyts.transformation: Transformation algorithms#

The pyts.transformation module includes transformation algorithms.

transformation.BagOfPatterns([window_size, ...])

Bag-of-patterns representation for time series.

transformation.BOSS([word_size, n_bins, ...])

Bag of Symbolic Fourier Approximation Symbols.

transformation.ROCKET([n_kernels, ...])

RandOm Convolutional KErnel Transformation.

transformation.ShapeletTransform([...])

Shapelet Transform Algorithm.

transformation.WEASEL([word_size, n_bins, ...])

Word ExtrAction for time SEries cLassification.

pyts.utils: Utility tools#

The pyts.utils module includes utility tools.

utils.segmentation(ts_size, window_size[, ...])

Compute the indices for Piecewise Agrgegate Approximation.

utils.windowed_view(X, window_size[, ...])

Return a windowed view of a 2D array.