Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning

Bo Tsuen Chen; Mu Yen Chen; Hsiu Sen Chiang; Chia Chen Chen

doi:10.1007/978-3-642-23863-5_39

Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning

Bo Tsuen Chen, Mu Yen Chen, Hsiu Sen Chiang, Chia Chen Chen

Department of Engineering Science

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

2 Citations (Scopus)

Abstract

The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts. In this paper, we applied entropy-based discretization partitioning to obtain optimized linguistic intervals setting for fuzzy time-series model. In order to evaluate our proposed approach, the dataset collected from Taiwan Stock Exchange (TAIEX). Finally, the experimental results showed that our proposed approach was effective in finding for the better linguistic intervals settings, when the entropy-based discretization partitioning is applied. Furthermore, the performances indicate that the proposed model is superior to the compared models suggested by Chen (1996) and Yu (2005) earlier. It is evident that the entropy partitioning is a good approach to obtain optimized linguistic intervals for fuzzy time-series models.

Original language	English
Title of host publication	Knowledge-Based and Intelligent Information and Engineering Systems - 15th International Conference, KES 2011, Proceedings
Pages	382-391
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-23863-5_39
Publication status	Published - 2011
Event	15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011 - Kaiserslautern, Germany Duration: 2011 Sept 12 → 2011 Sept 14

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6882 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011
Country/Territory	Germany
City	Kaiserslautern
Period	11-09-12 → 11-09-14

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-23863-5_39

Cite this

Chen, B. T., Chen, M. Y., Chiang, H. S., & Chen, C. C. (2011). Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning. In Knowledge-Based and Intelligent Information and Engineering Systems - 15th International Conference, KES 2011, Proceedings (PART 2 ed., pp. 382-391). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6882 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-23863-5_39

Chen, Bo Tsuen ; Chen, Mu Yen ; Chiang, Hsiu Sen et al. / Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning. Knowledge-Based and Intelligent Information and Engineering Systems - 15th International Conference, KES 2011, Proceedings. PART 2. ed. 2011. pp. 382-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning",

abstract = "The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts. In this paper, we applied entropy-based discretization partitioning to obtain optimized linguistic intervals setting for fuzzy time-series model. In order to evaluate our proposed approach, the dataset collected from Taiwan Stock Exchange (TAIEX). Finally, the experimental results showed that our proposed approach was effective in finding for the better linguistic intervals settings, when the entropy-based discretization partitioning is applied. Furthermore, the performances indicate that the proposed model is superior to the compared models suggested by Chen (1996) and Yu (2005) earlier. It is evident that the entropy partitioning is a good approach to obtain optimized linguistic intervals for fuzzy time-series models.",

author = "Chen, {Bo Tsuen} and Chen, {Mu Yen} and Chiang, {Hsiu Sen} and Chen, {Chia Chen}",

note = "Funding Information: Acknowledgments. The authors thank the support of National Scientific Council (NSC) of the Republic of China (ROC) to this work under Grant No. NSC-99-2410-H-025-011. Moreover, we also thank for STATSOFT STATISTICA 9.0 software to support related experiments.; 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011 ; Conference date: 12-09-2011 Through 14-09-2011",

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Chen, BT, Chen, MY, Chiang, HS & Chen, CC 2011, Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning. in Knowledge-Based and Intelligent Information and Engineering Systems - 15th International Conference, KES 2011, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6882 LNAI, pp. 382-391, 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011, Kaiserslautern, Germany, 11-09-12. https://doi.org/10.1007/978-3-642-23863-5_39

Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning. / Chen, Bo Tsuen; Chen, Mu Yen; Chiang, Hsiu Sen et al.
Knowledge-Based and Intelligent Information and Engineering Systems - 15th International Conference, KES 2011, Proceedings. PART 2. ed. 2011. p. 382-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6882 LNAI, No. PART 2).

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

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AU - Chen, Chia Chen

N1 - Funding Information: Acknowledgments. The authors thank the support of National Scientific Council (NSC) of the Republic of China (ROC) to this work under Grant No. NSC-99-2410-H-025-011. Moreover, we also thank for STATSOFT STATISTICA 9.0 software to support related experiments.

PY - 2011

Y1 - 2011

N2 - The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts. In this paper, we applied entropy-based discretization partitioning to obtain optimized linguistic intervals setting for fuzzy time-series model. In order to evaluate our proposed approach, the dataset collected from Taiwan Stock Exchange (TAIEX). Finally, the experimental results showed that our proposed approach was effective in finding for the better linguistic intervals settings, when the entropy-based discretization partitioning is applied. Furthermore, the performances indicate that the proposed model is superior to the compared models suggested by Chen (1996) and Yu (2005) earlier. It is evident that the entropy partitioning is a good approach to obtain optimized linguistic intervals for fuzzy time-series models.

AB - The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts. In this paper, we applied entropy-based discretization partitioning to obtain optimized linguistic intervals setting for fuzzy time-series model. In order to evaluate our proposed approach, the dataset collected from Taiwan Stock Exchange (TAIEX). Finally, the experimental results showed that our proposed approach was effective in finding for the better linguistic intervals settings, when the entropy-based discretization partitioning is applied. Furthermore, the performances indicate that the proposed model is superior to the compared models suggested by Chen (1996) and Yu (2005) earlier. It is evident that the entropy partitioning is a good approach to obtain optimized linguistic intervals for fuzzy time-series models.

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Chen BT, Chen MY, Chiang HS, Chen CC. Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning. In Knowledge-Based and Intelligent Information and Engineering Systems - 15th International Conference, KES 2011, Proceedings. PART 2 ed. 2011. p. 382-391. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-23863-5_39

Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning

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