Evolutionary time series segmentation for stock data mining

Fu Lai Korris Chung, Tak Chung Fu, Wing Pong Robert Luk, Vincent To Yee Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

40 Citations (Scopus)

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 an easier task. Most recent work on time series queries only concentrates 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 the users and the applications. In view of the facts 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 the 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.
Original languageEnglish
Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
Pages83-90
Number of pages8
Publication statusPublished - 1 Dec 2002
Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
Duration: 9 Dec 200212 Dec 2002

Conference

Conference2nd IEEE International Conference on Data Mining, ICDM '02
CountryJapan
CityMaebashi
Period9/12/0212/12/02

ASJC Scopus subject areas

  • Engineering(all)

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