# Approximating time series¶

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

# Bag-of-words transformation¶

Bag-of-words algorithms transform a sequence of symbols into a bag of words.

# Metrics¶

Specific metrics for time series have been developed. The examples below illustrate some of the implemented metrics.

# Multivariate time series¶

Specific algorithms for multivariate time series have been developed. The examples below illustrate some of the implemented ones.

# Preprocessing tools¶

Preprocessing data is a common task in machine learning. The examples below illustrate the preprocessing tools available in this module.

# Transformation algorithms¶

Transformation algorithms try to capture the most important information from time series using advanced transformation. They can be seen as complex feature extraction algorithms.