research-article

Pattern Discovery of Fuzzy Time Series for Financial Prediction

Authors:

Chiung-Hon Leon Lee,

Wen-Sung ChenAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 18, Issue 5

Pages 613 - 625

https://doi.org/10.1109/TKDE.2006.80

Published: 01 May 2006 Publication History

Abstract

A fuzzy time series data representation method based on the Japanese candlestick theory is proposed and used in assisting financial prediction. The Japanese candlestick theory is an empirical model of investment decision. The theory assumes that the candlestick patterns reflect the psychology of the market, and the investors can make their investment decision based on the identified candlestick patterns. We model the imprecise and vague candlestick patterns with fuzzy linguistic variables and transfer the financial time series data to fuzzy candlestick patterns for pattern recognition. A fuzzy candlestick pattern can bridge the gap between the investors and the system designer because it is visual, computable, and modifiable. The investors are not only able to understand the prediction process, but also to improve the efficiency of prediction results. The proposed approach is applied to financial time series forecasting problem for demonstration. By the prototype system which has been established, the investment expertise can be stored in the knowledge base, and the fuzzy candlestick pattern can also be identified automatically from a large amount of the financial trading data.

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Cited By

Tang YChen DZuo YLu XRanjan RZomaya AYao QLi X(2023)Enhanced Bayesian Factorization With Variant Scale Partitioning for Multivariate Time Series AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312877035:4(3832-3845)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3128770
Cagliero LFior JGarza P(2023)Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119493216:COnline publication date: 15-Apr-2023
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Pattern Discovery of Fuzzy Time Series for Financial Prediction

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 18, Issue 5

May 2006

143 pages

ISSN:1041-4347

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Copyright © 2006.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 May 2006

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Cited By

Tang YChen DZuo YLu XRanjan RZomaya AYao QLi X(2023)Enhanced Bayesian Factorization With Variant Scale Partitioning for Multivariate Time Series AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312877035:4(3832-3845)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3128770
Cagliero LFior JGarza P(2023)Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119493216:COnline publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119493
Carmona-Poyato ÁFernández-García NMadrid-Cuevas FMuñoz-Salinas RRomero-Ramírez F(2023)Optimal online time-series segmentationKnowledge and Information Systems10.1007/s10115-023-02029-866:4(2417-2438)Online publication date: 26-Dec-2023
https://dl.acm.org/doi/10.1007/s10115-023-02029-8
Naranjo RSantos M(2019)A fuzzy decision system for money investment in stock markets based on fuzzy candlesticks pattern recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.05.012133:C(34-48)Online publication date: 1-Nov-2019
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Ha MMoon BBosman P(2017)The evolution of neural network-based chart patternsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071192(1113-1120)Online publication date: 1-Jul-2017
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Ha MLee SMoon BNeumann FSutton A(2016)A Genetic Algorithm for Rule-based Chart Pattern Search in Stock Market PricesProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908828(909-916)Online publication date: 20-Jul-2016
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Garg BGarg R(2016)Enhanced accuracy of fuzzy time series model using ordered weighted aggregationApplied Soft Computing10.1016/j.asoc.2016.07.00248:C(265-280)Online publication date: 1-Nov-2016
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