Early Time-Series Classification Algorithms: An Empirical Comparison

TL;DR

This paper empirically compares six early time-series classification algorithms across diverse datasets, providing insights into their performance trade-offs and establishing a framework for evaluation and benchmarking in real-world applications.

Contribution

It introduces a comprehensive evaluation framework and benchmark for early time-series classification algorithms, including analysis on new life sciences and maritime datasets.

Findings

Algorithms vary in earliness and accuracy depending on data characteristics.

The framework facilitates fair comparison and performance analysis.

New datasets demonstrate real-world applicability of ETSC methods.

Abstract

Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. Such methods can be employed to obtain classification forecasts in many time-critical applications. However, available techniques are not equally suitable for every problem, since differentiations in the data characteristics can impact algorithm performance in terms of earliness, accuracy, F1-score, and training time. We evaluate six existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets originating from the life sciences and maritime domains. Our goal is to provide a framework for the evaluation and comparison of ETSC algorithms and to obtain intuition on how such approaches perform on real-life applications. The presented framework may also serve as a benchmark for new related techniques.