Publication: Identifying zigzag based perceptually important points for indexing financial time series
Issued Date
2009-11-17
Resource Type
Other identifier(s)
2-s2.0-70449382426
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of the 2009 8th IEEE International Conference on Cognitive Informatics, ICCI 2009. (2009), 295-301
Suggested Citation
Chaliaw Phetking, Mohd Noor Md Sap, Ali Selamat Identifying zigzag based perceptually important points for indexing financial time series. Proceedings of the 2009 8th IEEE International Conference on Cognitive Informatics, ICCI 2009. (2009), 295-301. doi:10.1109/COGINF.2009.5250725 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/27479
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Identifying zigzag based perceptually important points for indexing financial time series
Author(s)
Other Contributor(s)
Abstract
Financial time series often exhibit high degrees of fluctuation which are considered as noise in time series analysis. To remove noise, several lower bounding the Euclidean distance based dimensionality reduction methods are applied. But, however, these methods do not meet the constraint of financial time series analysis that wants to retain the important points and remove others. Therefore, although a number of methods can retain the important points in the financial time series reduction, but, however, they loss the nature of financial time series which consist of several uptrends, downtrends and sideway trends in different resolutions and in the zigzag directions. In this paper, we propose the Zigzag based Perceptually Important Point Identification method to collect those zigzag movement important points. Further, we propose Zigzag based Multiway Search Tree to index these important points. We evaluate our methods in time series dimensionality reduction. The results show the significant performance comparing to other original method. © 2009 IEEE.