Abstract
In order to enhance the forecast accuracy of multivariable fuzzy time series model for a small sample, a new multivariable fuzzy time series forecasting model was built based on the multidimensional information distribution technology. Then as an example, three variables (the time series data of total energy consumption, per capita GDP and SO2 emissions from 2001 to 2017 in China) were selected for the case analysis, which was used to verify the feasibility and to discuss the influence of the variation of fuzziness on the forecast accuracy of the model. Furthermore, a comparative analysis with the Markov model is made. The results show that (1) the suggested model can make up for the defects of small sample; (2) the predictive accuracy increases with the decrease of fuzziness; (3) the proposed model has higher forecast accuracy than the Markov model in forecasting SO2 emissions .
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Acknowledgement
This research is financially supported by Program for the soft science of Shanxi Province in China (No. 2017041025-2); and Program for the Philosophy and Social Sciences Research of Higher Learning Institutions of Shanxi (PSSR) in China (No. 2017314).
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Xue, Y., Li, X., Fu, H. (2020). Construction of Multivariable Fuzzy Time Series Model Based on Multidimensional Information Distribution Technology. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_97
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DOI: https://doi.org/10.1007/978-3-030-32456-8_97
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