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The purpose of time-series data mining is to try to extract all meaningful knowledge from the\n            <jats:italic>shape<\/jats:italic>\n            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\n            <jats:italic>representation<\/jats:italic>\n            techniques,\n            <jats:italic>distance<\/jats:italic>\n            measures, and\n            <jats:italic>indexing<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/2379776.2379788","type":"journal-article","created":{"date-parts":[[2012,12,11]],"date-time":"2012-12-11T13:13:42Z","timestamp":1355231622000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":792,"title":["Time-series data mining"],"prefix":"10.1145","volume":"45","author":[{"given":"Philippe","family":"Esling","sequence":"first","affiliation":[{"name":"Institut de Recherche et Coordination Acoustique\/Musique (IRCAM), Paris, France"}]},{"given":"Carlos","family":"Agon","sequence":"additional","affiliation":[{"name":"Institut de Recherche et Coordination Acoustique\/Musique (IRCAM), Paris, France"}]}],"member":"320","published-online":{"date-parts":[[2012,12,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 5th International Symposium on Intelligent Data Analysis (IDA 03)","author":"Abonyi J.","unstructured":"Abonyi , J. , Fell , B. , Nemeth , S. , and Arva , P . 2003. 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