Time series clustering with applications in stock market analysis

Pao Yue-kong Library Electronic Theses Database

Time series clustering with applications in stock market analysis

 

Author: Zhou, Peiyuan
Title: Time series clustering with applications in stock market analysis
Degree: M.Sc.
Year: 2011
Subject: Data mining.
Time-series analysis.
Multivariate analysis.
Stock exchanges -- Mathematical models.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: 81 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2473586
URI: http://theses.lib.polyu.edu.hk/handle/200/6412
Abstract: Data 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.

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