Stock time series pattern matching: Template-based vs. rule-based approaches

Tak chung Fu, Fu Lai Korris Chung, Wing Pong Robert Luk, Chak m. Ng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

83 Citations (Scopus)

Abstract

One of the major duties of financial analysts is technical analysis. It is necessary to locate the technical patterns in the stock price movement charts to analyze the market behavior. Indeed, there are two main problems: how to define those preferred patterns (technical patterns) for query and how to match the defined pattern templates in different resolutions. As we can see, defining the similarity between time series (or time series subsequences) is of fundamental importance. By identifying the perceptually important points (PIPs) directly from the time domain, time series and templates of different lengths can be compared. Three ways of distance measure, including Euclidean distance (PIP-ED), perpendicular distance (PIP-PD) and vertical distance (PIP-VD), for PIP identification are compared in this paper. After the PIP identification process, both template- and rule-based pattern-matching approaches are introduced. The proposed methods are distinctive in their intuitiveness, making them particularly user friendly to ordinary data analysts like stock market investors. As demonstrated by the experiments, the template- and the rule-based time series matching and subsequence searching approaches provide different directions to achieve the goal of pattern identification.
Original languageEnglish
Pages (from-to)347-364
Number of pages18
JournalEngineering Applications of Artificial Intelligence
Volume20
Issue number3
DOIs
Publication statusPublished - 1 Apr 2007

Keywords

  • Perceptually important point identification
  • Stock time series
  • Subsequence pattern matching
  • Technical pattern
  • Whole series pattern matching

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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