Time-series data mining

Abstract : In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series. The purpose of time-series data mining is to try to extract all meaningful knowledge from the shape of data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this article we intend to provide a survey of the techniques applied for time-series data mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time-series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures, and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submits various research trends and avenues that can be explored in the near future. We hope that this article can provide a broad and deep understanding of the time-series data mining research field.
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Article dans une revue
ACM Computing Surveys, Association for Computing Machinery, 2012, 45 (1), pp.12. 〈10.1145/2379776.2379788〉
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Contributeur : Philippe Esling <>
Soumis le : lundi 28 août 2017 - 15:56:32
Dernière modification le : dimanche 9 décembre 2018 - 01:27:02


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Philippe Esling, Carlos Agon. Time-series data mining. ACM Computing Surveys, Association for Computing Machinery, 2012, 45 (1), pp.12. 〈10.1145/2379776.2379788〉. 〈hal-01577883〉



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