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Section Navigation

  • Plotting a time series
  • Approximating time series
    • Discrete Fourier Transform
    • Multiple Coefficient Binning
    • Piecewise Aggregate Approximation
    • Symbolic Aggregate approXimation
  • Bag-of-words transformation
    • Bag of Words
    • Word Extractor
  • Classification algorithms
    • Bag-of-SFA Symbols in Vector Space (BOSSVS)
    • Learning Time-Series Shapelets
    • Symbolic Aggregate approXimation in Vector Space Model (SAX-VSM)
    • Time Series Bag-of-Features
    • Time Series Forest
  • Clustering time series
    • Time Series Clustering with DTW and BOSS
  • Dataset utilities
    • Loading the GunPoint dataset
    • Making a Cylinder-Bell-Funnel dataset
  • Decomposing time series
    • Singular Spectrum Analysis
    • Trend-Seasonal decomposition with Singular Spectrum Analysis
  • Imaging time series
    • Data set of Gramian angular fields
    • Data set of Markov transition fields
    • Data set of recurrence plots
    • Single Gramian angular field
    • Single Markov transition field
    • Single recurrence plot
  • Metrics
    • Dynamic Time Warping
    • Itakura parallelogram
    • Sakoe-Chiba band
  • Multivariate time series
    • Joint Recurrence Plot
    • WEASEL+MUSE
  • Preprocessing tools
    • Imputer
    • Scalers
    • Transformers
  • Transformation algorithms
    • Bag of Patterns
    • Bag-of-SFA Symbols (BOSS)
    • RandOm Convolutional KErnel Transform (ROCKET)
    • Shapelet Transform
    • Word ExtrAction for time SEries cLassification (WEASEL)
  • Introductory examples
  • Approximating time series

Approximating time series#

Approximation algorithms try to capture the most important information from time series. They can be seen as simple feature extraction algorithms.

Discrete Fourier Transform

Discrete Fourier Transform

Multiple Coefficient Binning

Multiple Coefficient Binning

Piecewise Aggregate Approximation

Piecewise Aggregate Approximation

Symbolic Aggregate approXimation

Symbolic Aggregate approXimation
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