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Neuromorphic Quantum-Based Adaptive Support Vector Regression for Tuning BWGC/NGARCH Forecast Model

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Advances in Neural Networks – ISNN 2007 (ISNN 2007)

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Abstract

A prediction model, called BPNN-weighted grey model and cumulated 3-point least square polynomial (BWGC), is used for resolving the overshoot effect; however, it may encounter volatility clustering due to the lack of localization property. Thus, we incorporate the non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC to compensate for the time-varying variance of residual errors when volatility clustering occurs. Furthermore, in order for adapting both models optimally, a neuromorphic quantum-based adaptive support vector regression (NQASVR) is schemed to regularize the coefficients for both BWGC and NGARCH linearly to improve the generalization and the localization at the same time effectively.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taitung University, Taitung, 950, Taiwan

    Bao Rong Chang

  2. Department of International Business, Shu-Te University, Kaohsiung, 824, Taiwan

    Hsiu Fen Tsai

Authors
  1. Bao Rong Chang
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  2. Hsiu Fen Tsai
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Editor information

Derong Liu Shumin Fei Zengguang Hou Huaguang Zhang Changyin Sun

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© 2007 Springer Berlin Heidelberg

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Chang, B.R., Tsai, H.F. (2007). Neuromorphic Quantum-Based Adaptive Support Vector Regression for Tuning BWGC/NGARCH Forecast Model. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_46

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  • DOI: https://doi.org/10.1007/978-3-540-72395-0_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72394-3

  • Online ISBN: 978-3-540-72395-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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