Rumena Komitova, Dominik Raabe, R. Rein, D. Memmert
{"title":"Time Series Data Mining for Sport Data: a Review","authors":"Rumena Komitova, Dominik Raabe, R. Rein, D. Memmert","doi":"10.2478/ijcss-2022-0008","DOIUrl":null,"url":null,"abstract":"Abstract Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data contain time information, it is natural to consider the temporal dimension in form of time series. However, in sports, the effective use of time series data mining techniques is still under development. The main goal of this paper is therefore to serve as an introduction to time series data mining and a glossary for interested researchers from the sports community. The paper gives an overview about current data mining tasks and tries to identify their potential research direction for further investigation. Furthermore, we want to draw more attention with respect to the importance of mining approaches with sport data and their particular challenges beyond usual time series data mining tasks.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":"21 1","pages":"17 - 31"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Science in Sport","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/ijcss-2022-0008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data contain time information, it is natural to consider the temporal dimension in form of time series. However, in sports, the effective use of time series data mining techniques is still under development. The main goal of this paper is therefore to serve as an introduction to time series data mining and a glossary for interested researchers from the sports community. The paper gives an overview about current data mining tasks and tries to identify their potential research direction for further investigation. Furthermore, we want to draw more attention with respect to the importance of mining approaches with sport data and their particular challenges beyond usual time series data mining tasks.