When is Early Classification of Time Series Meaningful?

TL;DR

This paper critically examines early classification of time series, revealing that current methods are often unrealistic for real-world applications due to vague problem definitions and unwarranted assumptions.

Contribution

The paper highlights fundamental issues in the problem formulation of early time series classification and provides insights and recommendations to improve research practices.

Findings

Current algorithms often rely on unrealistic assumptions.

Vague problem definitions lead to impractical solutions.

Recommendations for clearer problem framing and evaluation.

Abstract

Since its introduction two decades ago, there has been increasing interest in the problem of early classification of time series. This problem generalizes classic time series classification to ask if we can classify a time series subsequence with sufficient accuracy and confidence after seeing only some prefix of a target pattern. The idea is that the earlier classification would allow us to take immediate action, in a domain in which some practical interventions are possible. For example, that intervention might be sounding an alarm or applying the brakes in an automobile. In this work, we make a surprising claim. In spite of the fact that there are dozens of papers on early classification of time series, it is not clear that any of them could ever work in a real-world setting. The problem is not with the algorithms per se but with the vague and underspecified problem description. Essentially all algorithms make implicit and unwarranted assumptions about the problem that will ensure that they will be plagued by false positives and false negatives even if their results suggested that they could obtain near-perfect results. We will explain our findings with novel insights and experiments and offer recommendations to the community.