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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

56 Scopus Citations
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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1155-1162
Journal / PublicationProcedia Computer Science
Volume18
Online published1 Jun 2013
Publication statusPublished - 2013

Conference

Title13th Annual International Conference on Computational Science, ICCS 2013
PlaceSpain
CityBarcelona
Period5 - 7 June 2013

Link(s)

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.

Research Area(s)

  • Fuzzy time series, Genetic algorithm, Stock forecasting

Citation Format(s)

A novel stock forecasting model based on fuzzy time series and genetic algorithm. / Cai, Qi Sen; Zhang, Defu; Wu, Bo; Leung, Stehpen C.H.

In: Procedia Computer Science, Vol. 18, 2013, p. 1155-1162.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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