Browsing by Author "Ali Selamat"

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    Identifying zigzag based perceptually important points for indexing financial time series
    (2009) Chaliaw Phetking; Mohd Noor Md. Sap; Ali Selamat; C. Phetking; Faculty of Science and Technology, Rajabhat Suan Dusit Unviersity, Wachira, Dusit, Bangkok, 10170, Thailand; email: chaliaw-phe@dusit.ac.th
    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.
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    Index financial time series based on zigzag-perceptually important points
    (2010) Chawalsak Phetchanchai; Ali Selamat; Amjad Rehman; Tanzila Saba
    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 Science Publications.