Evolutionary segmentation of financial time series into subsequences

T. C. Fu, Fu Lai Korris Chung, Vincent To Yee Ng, Wing Pong Robert Luk

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

31 Citations (Scopus)


Time series data are difficult to manipulate. But when they can be transformed into meaningful symbols, it becomes an easy task to query and understand them. While most recent works in time series query only concentrate on how to identify a given pattern from a time series, they do not consider the problem of identifying a suitable set of time points based upon which the time series can be segmented in accordance with a given set of pattern templates,e.g., a set of technical analysis patterns for stock analysis. On the other hand, using fixed length segmentation is only a primitive approach to such kind of problem and hence a dynamic approach is preferred so that the time series can be segmented flexibly and effectively. In view of the fact that such a segmentation problem is actually an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary segmentation algorithm in this paper. Encouraging experimental results in segmenting the Hong Kong Hang Sen g Index using 22 technical analysis patterns are reported.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation, ICEC
Number of pages5
Publication statusPublished - 1 Jan 2001
EventCongress on Evolutionary Computation 2001 - Soul, Korea, Republic of
Duration: 27 May 200130 May 2001


ConferenceCongress on Evolutionary Computation 2001
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)


Dive into the research topics of 'Evolutionary segmentation of financial time series into subsequences'. Together they form a unique fingerprint.

Cite this