User guideΒΆ

  • 1. Introduction
    • 1.1. Introduction
    • 1.2. Mathematical formulation
    • 1.3. Challenge: lengths of time series
    • 1.4. Notations
  • 2. Classification of raw time series
    • 2.1. KNeighborsClassifier
    • 2.2. SAXVSM
    • 2.3. BOSSVS
    • 2.4. LearningShapelets
    • 2.5. TimeSeriesForest
    • 2.6. Time Series Bag-of-Features
  • 3. Extracting features from time series
    • 3.1. ShapeletTransform
    • 3.2. BagOfPatterns
    • 3.3. BOSS
    • 3.4. WEASEL
    • 3.5. ROCKET
  • 4. Imaging time series
    • 4.1. Recurrence Plot
    • 4.2. Gramian Angular Field
    • 4.3. Markov Transition Field
  • 5. Preprocessing utilities
    • 5.1. Imputing missing values
    • 5.2. Scaling
    • 5.3. Non-linear transformation
  • 6. Decomposing time series
    • 6.1. Singular Spectrum Analysis
  • 7. Approximating time series
    • 7.1. Piecewise Aggregate Approximation
    • 7.2. Symbolic Aggregate approXimation
    • 7.3. Discrete Fourier Transform
    • 7.4. Multiple Coefficient Binning
    • 7.5. Symbolic Fourier Approximation
  • 8. Bag of words for time series
    • 8.1. Bag of words
    • 8.2. Word Extractor
  • 9. Metrics for time series
    • 9.1. Classic Dynamic Time Warping
    • 9.2. Variants of Dynamic Time Warping
    • 9.3. Implementations
  • 10. Multivariate time series
    • 10.1. Classification
    • 10.2. Transformation
    • 10.3. Image
  • 11. Dataset loading utilities
    • 11.1. Simulated datasets
    • 11.2. Univariate time series: UCR repository
    • 11.3. Multivariate time series: UEA repository
 
  • ← Contributing guide
  • 1. Introduction →

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A Python Package for Time Series Classification

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Getting Started

  • Installation, testing and development
  • Contributing guide

Documentation

  • User guide
    • 1. Introduction
    • 2. Classification of raw time series
    • 3. Extracting features from time series
    • 4. Imaging time series
    • 5. Preprocessing utilities
    • 6. Decomposing time series
    • 7. Approximating time series
    • 8. Bag of words for time series
    • 9. Metrics for time series
    • 10. Multivariate time series
    • 11. Dataset loading utilities
  • API Documentation
  • Scikit-learn compatibility

Tutorial - Examples

  • Introductory examples
  • Approximating time series
  • Bag-of-words transformation
  • Classification algorithms
  • Clustering time series
  • Dataset utilities
  • Decomposing time series
  • Imaging time series
  • Metrics
  • Multivariate time series
  • Preprocessing tools
  • Transformation algorithms

Additional Information

  • Reproducibility
  • Change Log
  • Citation

Related Topics

  • Documentation overview
    • Previous: Contributing guide
    • Next: 1. Introduction

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