Applying trie-structure to improve dynamic time warping on time-series stock data analysis

An-Pin Chen*, Yi Chang Chen, Chih Yen Yeh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Dynamic time warping (DTW) is a robust but time-consuming on distance measure for time-series data similarity search. To speed up DTW for time-series data analysis, a new approach with trie-structure is introduced for the process of the DTW recognition steps, such as in financial stock pattern analysis. The final result shows that searching time has been reduced by this approach while applying trie-structure to DTW on time-series data analysis, especially on stock data.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics)
EditorsM.H. Hamza
Pages31-36
Number of pages6
StatePublished - 1 Dec 2004
EventProceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics - Innsbruck, Austria
Duration: 16 Feb 200418 Feb 2004

Publication series

NameProceedings of the IASTED International Conference. Applied Informatics

Conference

ConferenceProceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics
CountryAustria
CityInnsbruck
Period16/02/0418/02/04

Keywords

  • Dynamic time warping
  • Stock data
  • Time-series data
  • Trie-structure

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