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. |