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A Weighted Non-linear Grey Bernoulli Model for Forecasting Non-linear Economic Time Series with Small Data Sets

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  • Zheng-Xin Wang

    (Zhejiang University of Finance & Economics)

Abstract

To accurately forecast non-linear economic time series with small data sets, the weighted non-linear grey Bernoulli model (WNGBM) is built in this paper. Through the optimization of the power index and weights for accumulative generation, WNGBM can more actively adapt to non-linear fluctuations in the raw data than NGBM. A typical case of topological rolling prediction verifies that the WNGBM exhibits better non-linear prediction capabilities than other grey models. Furthermore, the forecasting performance of WNGBM is compared with that of Holt-Winters, Support Vector Regression (SVR), and BP Neural Network (BPNN) based on the Shanghai Stock Exchange(SSE) Composite Indices. Results indicate that WNGBM shows the best ability to fit non- linear data from small sample sizes, while it has a slightly higher error in the prediction of out-of-sample data for the SSE Composite Index than that of BPNN. The extreme values mean that the prediction curve of the Holt-Winters method generally deviates from the actual data, which leads to a greater prediction error.

Suggested Citation

  • Zheng-Xin Wang, 2017. "A Weighted Non-linear Grey Bernoulli Model for Forecasting Non-linear Economic Time Series with Small Data Sets," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 169-186.
    • Handle: RePEc:cys:ecocyb:v:50:y:2017:i:1:p:169-186
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    References listed on IDEAS

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    1. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    2. Chen, Chun-I, 2008. "Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 278-287.
    3. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
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    Cited by:

    1. Wang, Zheng-Xin & Jv, Yue-Qi, 2021. "A non-linear systematic grey model for forecasting the industrial economy-energy-environment system," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    2. Liu, Xiaomei & Xie, Naiming, 2019. "A nonlinear grey forecasting model with double shape parameters and its application," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 203-212.
    3. Ling-Ling Pei & Qin Li, 2019. "Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model," Sustainability, MDPI, vol. 11(5), pages 1-15, February.
    4. Wen-Ze Wu & Tao Zhang & Chengli Zheng, 2019. "A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP," Complexity, Hindawi, vol. 2019, pages 1-10, October.

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    More about this item

    Keywords

    grey systems theory; time series prediction; non-linear grey Bernoulli model; small data sets; stock exchange composite index.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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