@article {10.3844/jcssp.2010.1389.1395,
article_type = {journal},
title = {Index Financial Time Series Based on Zigzag-Perceptually Important Points},
author = {Phetchanchai, Chawalsak and Selamat, Ali and Rehman, Amjad and Saba, Tanzila},
volume = {6},
number = {12},
year = {2010},
month = {Nov},
pages = {1389-1395},
doi = {10.3844/jcssp.2010.1389.1395},
url = {https://thescipub.com/abstract/jcssp.2010.1389.1395},
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 = {Journal of Computer Science},
publisher = {Science Publications}
}