NDLI: A novel piecewise linear segmentation for time series

Content Provider	IEEE Xplore Digital Library
Author	Yongwei Ding Xiaohu Yang Kavs, A.J. Juefeng Li
Copyright Year	2010
Description	Author affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou, China (Yongwei Ding; Xiaohu Yang) \|\| State Street Technology (Zhejiang), Hangzhou, China (Kavs, A.J.; Juefeng Li)
Abstract	Time series representation is one of the fundamental tasks in Time Series Data Mining (TSDM). Due to the advantage of easy understanding and implementation, Piecewise Linear Representation (PLR) has been widely used in compression, indexing, and similarity measurement of time series data. In this paper, we introduce a novel online PLR segmentation method. It is based on determining the cumulative radian error for each data point. The proposed method is demonstrated by applying to real stock market indices data and shows its effectiveness and superiority.
Starting Page	52
Ending Page	55
File Size	102675
Page Count	4
File Format	PDF
ISBN	9781424455850
e-ISBN	9781424455867
DOI	10.1109/ICCAE.2010.5451780
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-26
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fluctuations Piecewise linear techniques piecewise linear representation Discrete Fourier transforms Educational institutions time series Transaction databases Discrete wavelet transforms Data mining radian Computer science cumulative effect segmentation Computational efficiency Stock markets
Content Type	Text
Resource Type	Article

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