Temporal association rule mining

Ting Feng Tan; Qing Guo Wang; Tian He Phang; Xian Li; Jiangshuai Huang; Dan Zhang

doi:10.1007/978-3-319-23862-3_24

Temporal association rule mining

Ting Feng Tan, Qing Guo Wang, Tian He Phang, Xian Li, Jiangshuai Huang, Dan Zhang

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

5 Citations (Scopus)

Abstract

A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks of Dow Jones Industrial Average (DJIA) in terms of highest daily volume and DJIA (index time series, expressed in points) are used to form the time-series database (TSDB) from 1994 to 2007. The main goal is to generate profitable trades by uncovering hidden knowledge from the TSDB. This hidden knowledge refers to temporal association rules, which represent the repeated relationships between events of the financial time series with time-parameter constraints: sliding time windows. Following an approach similar to Knowledge Discovery in Databases (KDD), the basic idea is to use frequent events to discover significant rules. Then, we propose the Multi-level Intensive Subset Learning (MIST) algorithm and use it to unveil the finer rules within the subset of the corresponding significant rules. Hypothesis testing is later applied to remove rules that are deemed to occur by chance.

Original language	English
Title of host publication	Intelligence Science and Big Data Engineering
Subtitle of host publication	Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers
Editors	Zhi-Hua Zhou, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang, Zhi-Yong Liu, Yanning Zhang, Xiaofei He, Xinbo Gao
Publisher	Springer Verlag
Pages	247-257
Number of pages	11
ISBN (Print)	9783319238616
DOIs	https://doi.org/10.1007/978-3-319-23862-3_24
Publication status	Published - 2015
Externally published	Yes
Event	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China Duration: 14 Jun 2015 → 16 Jun 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9243
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Country/Territory	China
City	Suzhou
Period	14/06/15 → 16/06/15

Keywords

DJIA
Events
Financial time series
Hypothesis testing
Knowledge discovery
Temporal data mining

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-23862-3_24

Cite this

Tan, T. F., Wang, Q. G., Phang, T. H., Li, X., Huang, J., & Zhang, D. (2015). Temporal association rule mining. In Z.-H. Zhou, B. Fu, F. Hu, Z. Zhang, Z.-Y. Liu, Y. Zhang, X. He, & X. Gao (Eds.), Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers (pp. 247-257). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9243). Springer Verlag. https://doi.org/10.1007/978-3-319-23862-3_24

Tan, Ting Feng ; Wang, Qing Guo ; Phang, Tian He et al. / Temporal association rule mining. Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. editor / Zhi-Hua Zhou ; Baochuan Fu ; Fuyuan Hu ; Zhancheng Zhang ; Zhi-Yong Liu ; Yanning Zhang ; Xiaofei He ; Xinbo Gao. Springer Verlag, 2015. pp. 247-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a5d459c33f5243fa910df980fda097ab,

title = "Temporal association rule mining",

abstract = "A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks of Dow Jones Industrial Average (DJIA) in terms of highest daily volume and DJIA (index time series, expressed in points) are used to form the time-series database (TSDB) from 1994 to 2007. The main goal is to generate profitable trades by uncovering hidden knowledge from the TSDB. This hidden knowledge refers to temporal association rules, which represent the repeated relationships between events of the financial time series with time-parameter constraints: sliding time windows. Following an approach similar to Knowledge Discovery in Databases (KDD), the basic idea is to use frequent events to discover significant rules. Then, we propose the Multi-level Intensive Subset Learning (MIST) algorithm and use it to unveil the finer rules within the subset of the corresponding significant rules. Hypothesis testing is later applied to remove rules that are deemed to occur by chance.",

keywords = "DJIA, Events, Financial time series, Hypothesis testing, Knowledge discovery, Temporal data mining",

author = "Tan, {Ting Feng} and Wang, {Qing Guo} and Phang, {Tian He} and Xian Li and Jiangshuai Huang and Dan Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 ; Conference date: 14-06-2015 Through 16-06-2015",

year = "2015",

doi = "10.1007/978-3-319-23862-3_24",

language = "English",

isbn = "9783319238616",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "247--257",

editor = "Zhi-Hua Zhou and Baochuan Fu and Fuyuan Hu and Zhancheng Zhang and Zhi-Yong Liu and Yanning Zhang and Xiaofei He and Xinbo Gao",

booktitle = "Intelligence Science and Big Data Engineering",

address = "Germany",

}

Tan, TF, Wang, QG, Phang, TH, Li, X, Huang, J & Zhang, D 2015, Temporal association rule mining. in Z-H Zhou, B Fu, F Hu, Z Zhang, Z-Y Liu, Y Zhang, X He & X Gao (eds), Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9243, Springer Verlag, pp. 247-257, 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, Suzhou, China, 14/06/15. https://doi.org/10.1007/978-3-319-23862-3_24

Temporal association rule mining. / Tan, Ting Feng; Wang, Qing Guo; Phang, Tian He et al.
Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. ed. / Zhi-Hua Zhou; Baochuan Fu; Fuyuan Hu; Zhancheng Zhang; Zhi-Yong Liu; Yanning Zhang; Xiaofei He; Xinbo Gao. Springer Verlag, 2015. p. 247-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9243).

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

TY - GEN

T1 - Temporal association rule mining

AU - Tan, Ting Feng

AU - Wang, Qing Guo

AU - Phang, Tian He

AU - Li, Xian

AU - Huang, Jiangshuai

AU - Zhang, Dan

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks of Dow Jones Industrial Average (DJIA) in terms of highest daily volume and DJIA (index time series, expressed in points) are used to form the time-series database (TSDB) from 1994 to 2007. The main goal is to generate profitable trades by uncovering hidden knowledge from the TSDB. This hidden knowledge refers to temporal association rules, which represent the repeated relationships between events of the financial time series with time-parameter constraints: sliding time windows. Following an approach similar to Knowledge Discovery in Databases (KDD), the basic idea is to use frequent events to discover significant rules. Then, we propose the Multi-level Intensive Subset Learning (MIST) algorithm and use it to unveil the finer rules within the subset of the corresponding significant rules. Hypothesis testing is later applied to remove rules that are deemed to occur by chance.

AB - A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks of Dow Jones Industrial Average (DJIA) in terms of highest daily volume and DJIA (index time series, expressed in points) are used to form the time-series database (TSDB) from 1994 to 2007. The main goal is to generate profitable trades by uncovering hidden knowledge from the TSDB. This hidden knowledge refers to temporal association rules, which represent the repeated relationships between events of the financial time series with time-parameter constraints: sliding time windows. Following an approach similar to Knowledge Discovery in Databases (KDD), the basic idea is to use frequent events to discover significant rules. Then, we propose the Multi-level Intensive Subset Learning (MIST) algorithm and use it to unveil the finer rules within the subset of the corresponding significant rules. Hypothesis testing is later applied to remove rules that are deemed to occur by chance.

KW - DJIA

KW - Events

KW - Financial time series

KW - Hypothesis testing

KW - Knowledge discovery

KW - Temporal data mining

UR - http://www.scopus.com/inward/record.url?scp=84945948658&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-23862-3_24

DO - 10.1007/978-3-319-23862-3_24

M3 - Conference contribution

AN - SCOPUS:84945948658

SN - 9783319238616

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 247

EP - 257

BT - Intelligence Science and Big Data Engineering

A2 - Zhou, Zhi-Hua

A2 - Fu, Baochuan

A2 - Hu, Fuyuan

A2 - Zhang, Zhancheng

A2 - Liu, Zhi-Yong

A2 - Zhang, Yanning

A2 - He, Xiaofei

A2 - Gao, Xinbo

PB - Springer Verlag

T2 - 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015

Y2 - 14 June 2015 through 16 June 2015

ER -

Tan TF, Wang QG, Phang TH, Li X, Huang J, Zhang D. Temporal association rule mining. In Zhou ZH, Fu B, Hu F, Zhang Z, Liu ZY, Zhang Y, He X, Gao X, editors, Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. Springer Verlag. 2015. p. 247-257. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23862-3_24

Temporal association rule mining

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Keywords

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