Deep Reinforcement Learning based Triggering Function for Early Classifiers of Time Series

TL;DR

This paper introduces a Reinforcement Learning approach to optimize early classification of time series, demonstrating significant performance improvements over existing methods across multiple datasets.

Contribution

It translates ECTS problems into RL frameworks, enabling the design of potentially more effective triggering functions using the same information.

Findings

RL-based triggering functions outperform handcrafted ones

The proposed Alert system significantly outperforms state-of-the-art methods

Different state space configurations can lead to better early classification performance

Abstract

Early Classification of Time Series (ECTS) has been recognized as an important problem in many areas where decisions have to be taken as soon as possible, before the full data availability, while time pressure increases. Numerous ECTS approaches have been proposed, based on different triggering functions, each taking into account various pieces of information related to the incoming time series and/or the output of a classifier. Although their performances have been empirically compared in the literature, no studies have been carried out on the optimality of these triggering functions that involve ``man-tailored'' decision rules. Based on the same information, could there be better triggering functions? This paper presents one way to investigate this question by showing first how to translate ECTS problems into Reinforcement Learning (RL) ones, where the very same information is used in the state space. A thorough comparison of the performance obtained by ``handmade'' approaches and their ``RL-based'' counterparts has been carried out. A second question investigated in this paper is whether a different combination of information, defining the state space in RL systems, can achieve even better performance. Experiments show that the system we describe, called \textsc{Alert}, significantly outperforms its state-of-the-art competitors on a large number of datasets.