Identifying Zigzag based Perceptually Important Points for indexing financial time series

C. Phetking, M. Sap, A. Selamat
{"title":"Identifying Zigzag based Perceptually Important Points for indexing financial time series","authors":"C. Phetking, M. Sap, A. Selamat","doi":"10.1109/COGINF.2009.5250725","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 8th IEEE International Conference on Cognitive Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COGINF.2009.5250725","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

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.
识别基于之字形的感知重要点为索引金融时间序列
金融时间序列往往表现出高度的波动,在时间序列分析中被认为是噪声。为了去除噪声,采用了几种基于欧几里得距离的下边界降维方法。但是,这些方法都不符合金融时间序列分析的要求,即要保留重点,去除了其他部分。因此,虽然许多方法可以保留金融时间序列约简中的重要点,但是,它们却失去了金融时间序列的性质,即由不同分辨率和锯齿方向的几个上升趋势,下降趋势和横盘趋势组成。在本文中,我们提出了基于锯齿形的感知重要点识别方法来收集锯齿形运动的重要点。进一步,我们提出了基于之字形的多路搜索树来索引这些重要的点。我们评估我们的方法在时间序列降维。结果表明,与其他原始方法相比,该方法具有显著的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信