TY  - JOUR
AU  - Phetchanchai, Chawalsak 
AU  - Selamat, Ali 
AU  - Rehman, Amjad 
AU  - Saba, Tanzila 
PY  - 2010
TI  - Index Financial Time Series Based on Zigzag-Perceptually Important Points
JF  - Journal of Computer Science
VL  - 6
IS  - 12
DO  - 10.3844/jcssp.2010.1389.1395
UR  - https://thescipub.com/abstract/jcssp.2010.1389.1395
AB  - 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).