Quick-start guide

For a list of functions and classes available in tslearn, please have a look at our API Reference.

• 1. Installation
  • 1.1. Using conda
  • 1.2. Using PyPI
  • 1.3. Using latest github-hosted version
  • 1.4. A note on requirements
• 2. Getting started
  • 2.1. Time series format
  • 2.2. Importing standard time series datasets
  • 2.3. Playing with your data
• 3. Methods for variable-length time series
  • 3.1. Classification
  • 3.2. Regression
  • 3.3. Nearest-neighbor search
  • 3.4. Clustering
  • 3.5. Barycenter computation
  • 3.6. Model selection
  • 3.7. Resampling
• 4. Backend selection and use
  • 4.1. Backend selection
  • 4.2. Use the backends
  • 4.3. Choose the backend used by metric functions
  • 4.4. Automatic differentiation
• 5. Integration with other Python packages
  • 5.1. scikit-learn
  • 5.2. pyts
  • 5.3. seglearn
  • 5.4. stumpy
  • 5.5. sktime
  • 5.6. pyflux
  • 5.7. tsfresh
  • 5.8. cesium
• 6. Contributing
  • 6.1. More details on Pull requests