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Applications of AR*-GRNN model for financial time series forecasting

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Abstract

AR* models contain Autoregressive Moving Average and Generalized Autoregressive Conditional Heteroscedastic class model which are widely used in time series. Recent researches in forecasting with Generalized Regression Neural Network (GRNN) suggest that GRNN can be a promising alternative to the linear and nonlinear time series models. In this paper, a model composed of AR* and GRNN is proposed to take advantage of their feathers in linear and nonlinear modeling. In the AR*-GRNN model, AR* modeling improves the forecasting performance of the combined model by capturing statistical and volatility information from the time series. The relative experiments testify that the combined model provides an effective way to improve forecasting performance which can be achieved by either of the models used separately.

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Authors and Affiliations

  1. College of Information Science and Technology of Donghua University, 1882 West Yan’an Road 324#, 200051, Shanghai, People’s Republic of China

    Weimin Li, Yishu Luo, Qin Zhu, Jianwei Liu & Jiajin Le

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  1. Weimin Li
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  2. Yishu Luo
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  3. Qin Zhu
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  4. Jianwei Liu
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  5. Jiajin Le
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Correspondence to Weimin Li.

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Li, W., Luo, Y., Zhu, Q. et al. Applications of AR*-GRNN model for financial time series forecasting. Neural Comput & Applic 17, 441–448 (2008). https://doi.org/10.1007/s00521-007-0131-9

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  • DOI: https://doi.org/10.1007/s00521-007-0131-9

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