Index Financial Time Series Based on Zigzag-Perceptually Important Points
Chawalsak Phetchanchai, Ali Selamat, Amjad Rehman and Tanzila Saba
DOI : 10.3844/jcssp.2010.1389.1395
Journal of Computer Science
Volume 6, Issue 12
Problem statement: Financial time series were usually large in size, unstructured and of high dimensionality. Since, the illustration of financial time series shape was typically characterized by a few number of important points. These important points moved in zigzag directions which could form technical patterns. However, these important points exhibited in different resolutions and difficult to determine. Approach: In this study, we proposed novel methods of financial time series indexing by considering their zigzag movement. The methods consist of two major algorithms: first, the identification of important points, namely the Zigzag-Perceptually Important Points (ZIPs) identification method and next, the indexing method namely Zigzag based M-ary Tree (ZM-Tree) to structure and organize the important points. Results: The errors of the tree building and retrieving compared to the original time series increased when the important points increased. The dimensionality reduction using ZM-Tree based on tree pruning and number of retrieved points techniques performed better when the number of important points increased. Conclusion: Our proposed techniques illustrated mostly acceptable performance in tree operations and dimensionality reduction comparing to existing similar technique like Specialize Binary Tree (SB-Tree).
© 2010 Chawalsak Phetchanchai, Ali Selamat, Amjad Rehman and Tanzila Saba. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.