A Brief Survey of Time Series Classification Algorithms

Dedicated algorithms specially designed for classifying time series

By geralt at pixabay

A common task for time series machine learning is classification. Given a set of time series with class labels, can we train a model to accurately predict the class of new time series?

Source: Univariate time series classification with sktime

There are many algorithms dedicated to time series classification! This means you don’t have wrangle your data into a scikit-learn classifier or to turn to deep learning to solve every time series classification task.

In this article, I will introduce five categories of time series classification algorithms with details of specific algorithms. These specific algorithms have been shown to perform better on average than a baseline classifier (KNN) over a large number of different datasets [1].

1. Distance-based (KNN with dynamic time warping)
2. Interval-based (TimeSeriesForest)
3. Dictionary-based (BOSS, cBOSS)
4. Frequency-based (RISE — like TimeSeriesForest but with other features)
5. Shapelet-based (Shapelet Transform Classifier)

I conclude with brief guidance on selecting an appropriate algorithm.

The algorithms described in this article have been implemented in the sktime python package.

Sktime: a Unified Python Library for Time Series Machine Learning

Why dedicated algorithms for time series?

Time series classification algorithms tend to perform better than tabular classifiers on time series classification problems.

A common, but problematic solution to time series classification is to treat each time point as a separate feature and directly apply a standard learning algorithm (e.g. scikit-learn classifiers). In this approach, the algorithm ignores…