Early Classification of Time Series: A Survey and Benchmark

TL;DR

This paper provides a comprehensive survey and benchmark of early classification methods for time series, introducing a systematic evaluation protocol and an open-source library for comparison.

Contribution

It introduces a taxonomy, evaluation dimensions, and extensive experimental results for nine state-of-the-art ECTS algorithms, along with an open-source benchmarking library.

Findings

Extensive experimental comparison of nine ECTS algorithms.

Identification of key components and evaluation criteria for ECTS methods.

Provision of an open-source library for standardized benchmarking.

Abstract

In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the cost of misclassification. This problem has been particularly studied in the case of time series, and is known as Early Classification of Time Series (ECTS). Although it has been the subject of a growing body of literature, there is still a lack of a systematic, shared evaluation protocol to compare the relative merits of the various existing methods. In this paper, we highlight the two components of an ECTS system: decision and prediction, and focus on the approaches that separate them. This document begins by situating these methods within a principle-based taxonomy. It defines dimensions for organizing their evaluation and then reports the results of a very extensive set of experiments along these dimensions involving nine state-of-the-art ECTS algorithms. In addition, these and other experiments can be carried out using an open-source library in which most of the existing ECTS algorithms have been implemented (see https://github.com/ML-EDM/ml_edm).