A review on distance based time series classification

TL;DR

This review comprehensively examines distance-based methods for time series classification, highlighting recent advances, different approaches, and challenges such as kernel positive semi-definiteness.

Contribution

It provides a taxonomy of distance-based time series classification methods and discusses their strengths, weaknesses, and recent developments.

Findings

Distance-based methods are effective for time series classification.

New approaches outperform traditional 1-NN methods.

Transforming series into feature vectors or kernels enhances classifier performance.

Abstract

Time series classification is an increasing research topic due to the vast amount of time series data that are being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to it simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.