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Predicting Financial Time Series Data Using Hybrid Model

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Intelligent Systems and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 650))

Abstract

Prediction of financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and their dynamic nature. Support vector regression (SVR), Support vector machine (SVM) and back propagation neural network (BPNN) are the most popular data mining techniques in prediction financial time series. In this paper a hybrid combination model is introduced to combine the three models and to be most beneficial of them all. Quantization factor is used in this paper for the first time to improve the single SVM and SVR prediction output. And also genetic algorithm (GA) used to determine the weights of the proposed model. FTSE100, S&P 500 and Nikkei 225 daily index closing prices are used to evaluate the proposed model performance. The proposed hybrid model numerical results shows the outperform result over all other single model, traditional simple average combiner and the traditional time series model Autoregressive (AR).

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Author information

Authors and Affiliations

  1. Electronic and Computer Engineering Department, Brunel University, Uxbridge, London, UB8 3PH, UK

    Bashar Al-hnaity & Maysam Abbod

Authors
  1. Bashar Al-hnaity
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  2. Maysam Abbod
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Correspondence to Bashar Al-hnaity .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Al-hnaity, B., Abbod, M. (2016). Predicting Financial Time Series Data Using Hybrid Model. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. Studies in Computational Intelligence, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-33386-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-33386-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33384-7

  • Online ISBN: 978-3-319-33386-1

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