The k-nearest neighbour-based GMDH prediction model and its applications |
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| Authors: | Qiumin Li Yixiang Tian Gaoxun Zhang |
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| Affiliation: | 1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China;2. Department of Arts and Science, Chengdu College of University of Electronic Science and Technology of China, Chengdu, China |
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| Abstract: | This paper centres on a new GMDH (group method of data handling) algorithm based on the k-nearest neighbour (k-NN) method. Instead of the transfer function that has been used in traditional GMDH, the k-NN kernel function is adopted in the proposed GMDH to characterise relationships between the input and output variables. The proposed method combines the advantages of the k-nearest neighbour (k-NN) algorithm and GMDH algorithm, and thus improves the predictive capability of the GMDH algorithm. It has been proved that when the bandwidth of the kernel is less than a certain constant C, the predictive capability of the new model is superior to that of the traditional one. As an illustration, it is shown that the new method can accurately forecast consumer price index (CPI). |
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| Keywords: | k-NN GMDH non-parametric bias forecasting |
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