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

  • Authors:

  • Tak-chung Fu;Fu-lai Chung;Robert Luk;Chak-man Ng

  • Affiliations:

  • Department of Computing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong;Department of Computing and Information Management, Hong Kong Institute of Vocational Education Chai Wan, 30 Shing Tai Road, Chai Wan, Hong Kong

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2007

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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.