Identifying zigzag based perceptually important points for indexing financial time series

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 financia...

Full description

Saved in:
Bibliographic Details
Main Authors: Chaliaw Phetking, Mohd Noor Md Sap, Ali Selamat
Other Authors: Mahidol University
Format: Conference or Workshop Item
Published: 2018
Subjects:
Online Access:https://repository.li.mahidol.ac.th/handle/123456789/27479
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Mahidol University
Description
Summary: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.