Early and Revocable Time Series Classification

TL;DR

This paper introduces a novel framework for early time series classification that allows decision revocation, demonstrating improved performance over traditional irrevocable methods through extensive experiments.

Contribution

It proposes a new cost-based framework for revocable time series classification and develops two approaches, one considering decision change costs, with extensive empirical validation.

Findings

Revoking decisions improves classification performance.

Considering change costs yields even better results.

Revocable classification outperforms irrevocable methods.

Abstract

Many approaches have been proposed for early classification of time series in light of itssignificance in a wide range of applications including healthcare, transportation and fi-nance. Until now, the early classification problem has been dealt with by considering onlyirrevocable decisions. This paper introduces a new problem calledearly and revocabletimeseries classification, where the decision maker can revoke its earlier decisions based on thenew available measurements. In order to formalize and tackle this problem, we propose anew cost-based framework and derive two new approaches from it. The first approach doesnot consider explicitly the cost of changing decision, while the second one does. Exten-sive experiments are conducted to evaluate these approaches on a large benchmark of realdatasets. The empirical results obtained convincingly show (i) that the ability of revok-ing decisions significantly improves performance over the irrevocable regime, and (ii) thattaking into account the cost of changing decision brings even better results in general.Keywords:revocable decisions, cost estimation, online decision making