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

Authors:
Bao Rong Chang;Hsiu Fen Tsai
Affiliations:
Department of Computer Science and Information Engineering, National Taitung University, Taitung, 950, Taiwan;Department of International Business, Shu-Te University, Kaohsiung, 824, Taiwan
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
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.