Stock time series visualization based on data point importance

Tak chung Fu, Fu Lai Korris Chung, Ka yan Kwok, Chak man Ng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


Time series visualization is a fundamental task in most financial applications. A framework that can reduce the dimensionality of the time series data, sufficiently accurate so that it can capture the actual shape of the time series but, at the same time, salient points will not be smoothed out can take advantage of charting the raw time series data. On the other hand, it is preferable that the representation framework can handle the multi-resolution problem rather than reduce the dimension to a fixed level only. In this paper, a framework that represents and visualizes time series data based on data point importance is proposed. Furthermore, discovering frequently appearing and surprising patterns are non-trivial tasks in financial applications. A method for discovering patterns across different resolutions is proposed. The proposed method is based on a modified version of VizTree. By converting the time series to symbol string based on data point importance, the potential patterns with different lengths can be encoded in the VizTree for visual pattern discovery while the important points and the overall shape of the time series patterns can be preserved even under a high compression ratio. Various experiments were conducted to evaluate the performance of the proposed framework. One may find it particularly attractive in financial applications like stock data analysis.
Original languageEnglish
Pages (from-to)1217-1232
Number of pages16
JournalEngineering Applications of Artificial Intelligence
Issue number8
Publication statusPublished - 1 Dec 2008


  • Multi-resolution
  • Pattern discovery
  • Stock time series
  • Time series visualization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


Dive into the research topics of 'Stock time series visualization based on data point importance'. Together they form a unique fingerprint.

Cite this