A novel stock forecasting model based on fuzzy time series and genetic algorithm

Qi Sen Cai; Defu Zhang; Bo Wu; Stehpen C.H. Leung

doi:10.1016/j.procs.2013.05.281

A novel stock forecasting model based on fuzzy time series and genetic algorithm

Qi Sen Cai, Defu Zhang, Bo Wu, Stehpen C.H. Leung

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

74 Citations (Scopus)

75 Downloads (CityUHK Scholars)

Abstract

Stock market has been developed for over twenty years, and has gone deeply into all aspects of daily economic life and attracted more and more investors' attentions. Therefore, researches on finding internal rules and establishing an efficient stock forecast model to help investors minimize risks and maximize returns are very practical and amazing. In this paper, a hybrid model FTSGA based on fuzzy time series and genetic algorithm is proposed. FTSGA improves the performance by applying the operations of genetic algorithm such as selection, crossover and mutation to iteratively search a good discourse partition. TAIEX is selected as the experimental data set. And experimental results show that comparing with other models based on fuzzy time series FTSGA can greatly reduce the root mean square error and improve accuracy. © 2013 The Authors. Published by Elsevier B.V.

Original language	English
Pages (from-to)	1155-1162
Journal	Procedia Computer Science
Volume	18
Online published	1 Jun 2013
DOIs	https://doi.org/10.1016/j.procs.2013.05.281
Publication status	Published - 2013
Event	13th Annual International Conference on Computational Science, ICCS 2013 - Barcelona, Spain Duration: 5 Jun 2013 → 7 Jun 2013

Research Keywords

Fuzzy time series
Genetic algorithm
Stock forecasting

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 3.0. https://creativecommons.org/licenses/by-nc-nd/3.0/

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title = "A novel stock forecasting model based on fuzzy time series and genetic algorithm",

abstract = "Stock market has been developed for over twenty years, and has gone deeply into all aspects of daily economic life and attracted more and more investors' attentions. Therefore, researches on finding internal rules and establishing an efficient stock forecast model to help investors minimize risks and maximize returns are very practical and amazing. In this paper, a hybrid model FTSGA based on fuzzy time series and genetic algorithm is proposed. FTSGA improves the performance by applying the operations of genetic algorithm such as selection, crossover and mutation to iteratively search a good discourse partition. TAIEX is selected as the experimental data set. And experimental results show that comparing with other models based on fuzzy time series FTSGA can greatly reduce the root mean square error and improve accuracy. {\textcopyright} 2013 The Authors. Published by Elsevier B.V.",

keywords = "Fuzzy time series, Genetic algorithm, Stock forecasting",

author = "Cai, {Qi Sen} and Defu Zhang and Bo Wu and Leung, {Stehpen C.H.}",

year = "2013",

doi = "10.1016/j.procs.2013.05.281",

language = "English",

volume = "18",

pages = "1155--1162",

journal = "Procedia Computer Science",

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publisher = "Elsevier B.V.",

note = "13th Annual International Conference on Computational Science, ICCS 2013 ; Conference date: 05-06-2013 Through 07-06-2013",

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TY - JOUR

T1 - A novel stock forecasting model based on fuzzy time series and genetic algorithm

AU - Cai, Qi Sen

AU - Zhang, Defu

AU - Wu, Bo

AU - Leung, Stehpen C.H.

PY - 2013

Y1 - 2013

N2 - Stock market has been developed for over twenty years, and has gone deeply into all aspects of daily economic life and attracted more and more investors' attentions. Therefore, researches on finding internal rules and establishing an efficient stock forecast model to help investors minimize risks and maximize returns are very practical and amazing. In this paper, a hybrid model FTSGA based on fuzzy time series and genetic algorithm is proposed. FTSGA improves the performance by applying the operations of genetic algorithm such as selection, crossover and mutation to iteratively search a good discourse partition. TAIEX is selected as the experimental data set. And experimental results show that comparing with other models based on fuzzy time series FTSGA can greatly reduce the root mean square error and improve accuracy. © 2013 The Authors. Published by Elsevier B.V.

AB - Stock market has been developed for over twenty years, and has gone deeply into all aspects of daily economic life and attracted more and more investors' attentions. Therefore, researches on finding internal rules and establishing an efficient stock forecast model to help investors minimize risks and maximize returns are very practical and amazing. In this paper, a hybrid model FTSGA based on fuzzy time series and genetic algorithm is proposed. FTSGA improves the performance by applying the operations of genetic algorithm such as selection, crossover and mutation to iteratively search a good discourse partition. TAIEX is selected as the experimental data set. And experimental results show that comparing with other models based on fuzzy time series FTSGA can greatly reduce the root mean square error and improve accuracy. © 2013 The Authors. Published by Elsevier B.V.

KW - Fuzzy time series

KW - Genetic algorithm

KW - Stock forecasting

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84895863486&origin=recordpage

U2 - 10.1016/j.procs.2013.05.281

DO - 10.1016/j.procs.2013.05.281

M3 - RGC 21 - Publication in refereed journal

SN - 1877-0509

VL - 18

SP - 1155

EP - 1162

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 13th Annual International Conference on Computational Science, ICCS 2013

Y2 - 5 June 2013 through 7 June 2013

ER -

A novel stock forecasting model based on fuzzy time series and genetic algorithm

Abstract

Research Keywords

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