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dc.contributorDepartment of Computingen_US
dc.creatorZhou, Peiyuan-
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleTime series clustering with applications in stock market analysisen_US
dcterms.abstractData mining is usually be defined as the process of discovering useful knowledge from a database. The function of data mining includes classification, clustering and so on. In the last few years, analyze about time series data is at the leading edge of data mining. Its achievements has been used in many fields like Multi-media, biomedical science, and finance and the research on multivariate time series data mining is also receiving more and more attention. However, the high dimension character of time series data makes algorithm of data mining more complicated. As a result, how to deal with multivariate time series data has become an urgent problem to be solved. This paper specify two main algorithm to do analysis for multi-dimension time series data and use stock data as an application to explain the algorithm. The research mainly focuses on the following aspects. Firstly, for one stock we hope to do predication which can let us know the values or situations on the next day. Secondly, for a large number of stocks, we want to do clustering for them to partition the different stocks into different clusters. We can avoid investment risk through choosing the stocks from different clusters. On the other hand, the algorithm is also useful for other sequential data like genes chain, meteorological data and so on.en_US
dcterms.extent81 leaves : ill. ; 30 cm.en_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHData mining.en_US
dcterms.LCSHTime-series analysis.en_US
dcterms.LCSHMultivariate analysis.en_US
dcterms.LCSHStock exchanges -- Mathematical models.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/6412