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

An-Pin Chen; Yi Chang Chen; Chih Yen Yeh

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

Institute of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	American English
Title of host publication	Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics)
Editors	M.H. Hamza
Pages	31-36
Number of pages	6
State	Published - 2004
Event	Proceedings 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 2004 → 18 Feb 2004

Publication series

Name	Proceedings of the IASTED International Conference. Applied Informatics

Conference

Conference	Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics
Country/Territory	Austria
City	Innsbruck
Period	16/02/04 → 18/02/04

Keywords

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

Cite this

Chen, A.-P., Chen, Y. C., & Yeh, C. Y. (2004). Applying trie-structure to improve dynamic time warping on time-series stock data analysis. In M. H. Hamza (Ed.), Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics) (pp. 31-36). Article 411-278 (Proceedings of the IASTED International Conference. Applied Informatics).

Chen, An-Pin ; Chen, Yi Chang ; Yeh, Chih Yen. / Applying trie-structure to improve dynamic time warping on time-series stock data analysis. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics). editor / M.H. Hamza. 2004. pp. 31-36 (Proceedings of the IASTED International Conference. Applied Informatics).

@inproceedings{dc249d1693444a4c9bf437b860524c56,

title = "Applying trie-structure to improve dynamic time warping on time-series stock data analysis",

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.",

keywords = "Dynamic time warping, Stock data, Time-series data, Trie-structure",

author = "An-Pin Chen and Chen, {Yi Chang} and Yeh, {Chih Yen}",

year = "2004",

language = "American English",

isbn = "088986375X",

series = "Proceedings of the IASTED International Conference. Applied Informatics",

pages = "31--36",

editor = "M.H. Hamza",

booktitle = "Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics)",

note = "Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics ; Conference date: 16-02-2004 Through 18-02-2004",

}

Chen, A-P, Chen, YC & Yeh, CY 2004, Applying trie-structure to improve dynamic time warping on time-series stock data analysis. in MH Hamza (ed.), Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics)., 411-278, Proceedings of the IASTED International Conference. Applied Informatics, pp. 31-36, Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics, Innsbruck, Austria, 16/02/04.

Applying trie-structure to improve dynamic time warping on time-series stock data analysis. / Chen, An-Pin; Chen, Yi Chang; Yeh, Chih Yen.
Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics). ed. / M.H. Hamza. 2004. p. 31-36 411-278 (Proceedings of the IASTED International Conference. Applied Informatics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Applying trie-structure to improve dynamic time warping on time-series stock data analysis

AU - Chen, An-Pin

AU - Chen, Yi Chang

AU - Yeh, Chih Yen

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Dynamic time warping

KW - Stock data

KW - Time-series data

KW - Trie-structure

UR - http://www.scopus.com/inward/record.url?scp=11144339417&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:11144339417

SN - 088986375X

SN - 9780889863750

T3 - Proceedings of the IASTED International Conference. Applied Informatics

SP - 31

EP - 36

BT - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics)

A2 - Hamza, M.H.

T2 - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics

Y2 - 16 February 2004 through 18 February 2004

ER -

Chen AP, Chen YC, Yeh CY. Applying trie-structure to improve dynamic time warping on time-series stock data analysis. In Hamza MH, editor, Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics). 2004. p. 31-36. 411-278. (Proceedings of the IASTED International Conference. Applied Informatics).

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

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Publication series

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Keywords

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