API Documentation¶
Full API documentation of the pyts Python package.
pyts.approximation: Approximation algorithms¶
The pyts.approximation module includes approximation algorithms.
approximation.PiecewiseAggregateApproximation([…]) |
Piecewise Aggregate Approximation. |
approximation.SymbolicAggregateApproximation([…]) |
Symbolic Aggregate approXimation. |
approximation.DiscreteFourierTransform([…]) |
Discrete Fourier Transform. |
approximation.MultipleCoefficientBinning([…]) |
Bin continuous data into intervals column-wise. |
approximation.SymbolicFourierApproximation([…]) |
Symbolic Fourier Approximation. |
pyts.bag_of_words: Bag-of-words algorithms¶
The pyts.bag_of_words module includes bag-of-patterns algorithms.
bag_of_words.BagOfWords([window_size, …]) |
Transform time series into bag of words. |
pyts.classification: Classification algorithms¶
The pyts.classification module includes classification algorithms.
classification.KNeighborsClassifier([…]) |
k-nearest neighbors classifier. |
classification.SAXVSM([n_bins, strategy, …]) |
Classifier based on SAX-VSM representation and tf-idf statistics. |
classification.BOSSVS([word_size, n_bins, …]) |
Bag-of-SFA Symbols in Vector Space. |
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.RecurrencePlot([dimension, …]) |
Recurrence Plot. |
image.GramianAngularField([image_size, …]) |
Gramian Angular Field. |
image.MarkovTransitionField([image_size, …]) |
Markov Transition Field. |
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.dtw_classic([x, y, dist, …]) |
Classic Dynamic Time Warping (DTW) distance between two time series. |
metrics.dtw_region([x, y, dist, region, …]) |
Dynamic Time Warping (DTW) distance with a constraint region. |
metrics.dtw_sakoechiba([x, y, dist, …]) |
Dynamic Time Warping (DTW) distance with Sakoe-Chiba band constraint. |
metrics.dtw_itakura([x, y, dist, max_slope, …]) |
Dynamic Time Warping distance with Itakura parallelogram constraint. |
metrics.dtw_multiscale(x, y[, dist, …]) |
Multiscale Dynamic Time Warping distance. |
metrics.dtw_fast(x, y[, dist, radius, …]) |
Fast Dynamic Time Warping distance. |
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 DTW 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.StandardScaler([with_mean, …]) |
Standardize time series by removing mean and scaling to unit variance. |
preprocessing.MinMaxScaler([sample_range]) |
Transforms samples by scaling each sample to a given range. |
preprocessing.MaxAbsScaler() |
Scale each sample by its maximum absolute value. |
preprocessing.RobustScaler([with_centering, …]) |
Scale samples using statistics that are robust to outliers. |
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.BOSS([word_size, n_bins, …]) |
Bag of Symbolic Fourier Approximation Symbols. |
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. |
