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

Zheng-Xin Wang
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Zheng-Xin Wang: Zhejiang University of Finance & Economics

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2017, vol. 51, issue 1, 169-186

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.

Keywords: grey systems theory; time series prediction; non-linear grey Bernoulli model; small data sets; stock exchange composite index. (search for similar items in EconPapers)
JEL-codes: C63 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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