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Author Fu-lai Chung ♦ Tak-chung Fu ♦ Luk, R. ♦ Ng, V.
Sponsorship IEEE Comput. Soc. Tech. Committee on Pattern Analysis & Machine Intelligence (TCPAMI) ♦ IEEE Comput. Soc. Tech. Committee on Computational Intelligence (TCCI)
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2002
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Data mining ♦ Time series analysis ♦ Transaction databases ♦ Pattern analysis ♦ Controllability ♦ Evolutionary computation ♦ Pattern matching ♦ Testing ♦ Research and development ♦ Shape
Abstract Stock data in the form of multiple time series are difficult to process, analyze and mine. However, when they can be transformed into meaningful symbols like technical patterns, it becomes easier. Most recent work on time series queries concentrates only on how to identify a given pattern from a time series. Researchers do not consider the problem of identifying a suitable set of time points for segmenting the time series in accordance with a given set of pattern templates (e.g., a set of technical patterns for stock analysis). On the other hand, using fixed length segmentation is a primitive approach to this problem; hence, a dynamic approach (with high controllability) is preferred so that the time series can be segmented flexibly and effectively according to the needs of users and applications. In view of the fact that such a segmentation problem is an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary time series segmentation algorithm. This approach allows a sizeable set of stock patterns to be generated for mining or query. In addition, defining the similarity between time series (or time series segments) is of fundamental importance in fitness computation. By identifying perceptually important points directly from the time domain, time series segments and templates of different lengths can be compared and intuitive pattern matching can be carried out in an effective and efficient manner. Encouraging experimental results are reported from tests that segment the time series of selected Hong Kong stocks.
Description Author affiliation: Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China (Fu-lai Chung; Tak-chung Fu; Luk, R.; Ng, V.)
ISBN 0769517544
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2002-12-09
Publisher Place Japan
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 579.93 kB
Page Count 8
Starting Page 83
Ending Page 90

Source: IEEE Xplore Digital Library