Research Article Open Access

Index Financial Time Series Based on Zigzag-Perceptually Important Points

Chawalsak Phetchanchai, Ali Selamat, Amjad Rehman and Tanzila Saba

Abstract

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).

Journal of Computer Science
Volume 6 No. 12, 2010, 1389-1395

DOI: https://doi.org/10.3844/jcssp.2010.1389.1395

Submitted On: 9 September 2010 Published On: 2 November 2010

How to Cite: Phetchanchai, C., Selamat, A., Rehman, A. & Saba, T. (2010). Index Financial Time Series Based on Zigzag-Perceptually Important Points. Journal of Computer Science, 6(12), 1389-1395. https://doi.org/10.3844/jcssp.2010.1389.1395

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Keywords

  • Financial time series indexing
  • important points
  • ZM-Tree
  • time series