LIPIcs.TIME.2023.18.pdf
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A Benchmark for Early Time-Series Classification (Extended Abstract)
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Charilaos Akasiadis 
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30th International Symposium on Temporal Representation and Reasoning (TIME 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Symposium on Temporal Representation and Reasoning (TIME) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-18
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Petro-Foti Kamberi, Evgenios Kladis, and Charilaos Akasiadis. A Benchmark for Early Time-Series Classification (Extended Abstract). In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 18:1-18:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.TIME.2023.18
https://doi.org/10.4230/LIPIcs.TIME.2023.18
Abstract
The objective of Early Time-Series Classification (ETSC) is to predict the class of incoming time-series by observing the fewest time-points possible. Although many approaches have been proposed in the past, not all techniques are suitable for every problem type. In particular, the characteristics of the input data may impact performance. To aid researchers and developers with deciding which kind of method suits their needs best, we developed a framework that allows the comparison of five existing ETSC algorithms, and also introduce a new method that is based on the selective truncation of time-series principle. To promote results reproducibility and the alignment of algorithm comparisons, we also include a bundle of datasets originating from real-world time-critical applications, and for which the application of ETSC algorithms can be considered quite valuable.
Subject Classification
ACM Subject Classification
- Computing methodologies → Machine learning algorithms
Keywords
- Time-series analysis
- Classification
- Benchmark
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References
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