| 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: | Department of Computing |
| Pages: | 81 leaves : ill. ; 30 cm. |
| Language: | English |
| 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. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b24735863.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.42 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/6412