When is Early Classification of Time Series Meaningful?
| Autor: | Audrey Der, Renjie Wu, Eamonn Keogh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Series (mathematics) Computer science business.industry False positives and false negatives Machine learning computer.software_genre Computer Science Applications Machine Learning (cs.LG) Prefix Computational Theory and Mathematics Action (philosophy) Ask price Intervention (counseling) Subsequence Spite Artificial intelligence business computer Information Systems |
| Popis: | 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. Full paper accepted by IEEE TKDE, extended abstract accepted by IEEE ICDE 2022 |
| Databáze: | OpenAIRE |
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