Advertisement

Construction of Multivariable Fuzzy Time Series Model Based on Multidimensional Information Distribution Technology

  • Ye XueEmail author
  • Xiaoxiao Li
  • Hengchun Fu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

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 .

Keywords

Multidimensional information distribution Fuzzy approximate reasoning Multivariable fuzzy time series Emission of SO2 

Notes

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).

References

  1. 1.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley Press, Hoboken (2015)zbMATHGoogle Scholar
  2. 2.
    Haldrup, N., Nielsen, F.S., Nielsen, M.Ø.: A vector autoregressive model for electricity prices subject to long memory and regime switching. Energy Econ. 32(5), 1044–1058 (2010)CrossRefGoogle Scholar
  3. 3.
    Mellit, A., Pavan, A.M., Benghanem, M.: Least squares support vector machine for short-term prediction of meteorological time series. Theoret. Appl. Climatol. 111(1–2), 297–307 (2013)CrossRefGoogle Scholar
  4. 4.
    Zecchin, C., Facchinetti, A., Sparacino, G., De Nicolao, G., Cobelli, C.: A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5653–5656. IEEE Press, New York (2011)Google Scholar
  5. 5.
    Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets Syst. 54(3), 269–277 (1993)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Avazbeigi, M., Doulabi, S.H.H., Karimi, B.: Choosing the appropriate order in fuzzy time series: a new N-factor fuzzy time series for prediction of the auto industry production. Expert Syst. Appl. 37(8), 5630–5639 (2010)CrossRefGoogle Scholar
  7. 7.
    Xue, Y.: A new fuzzy time series model based on fuzzy information optimization. Stat. Decis. 416(20), 12–15 (2014)Google Scholar
  8. 8.
    Shuai, Y., Song, T.L., Wang, J.P., Zhan, W.B.: Modified fuzzy time series model interval partitioning algorithm. Comput. Eng. Des. 38(2), 379–383 + 394 (2017)Google Scholar
  9. 9.
    Huang, C.F., Shi, Y.: Towards Efficient Fuzzy Information Processing Using the Principle of Information Diffusion. Physical-Verlag Press, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Wu, B., Tse, S., Hsu, Y.: A new approach of bivariate fuzzy time series: with applications to the stock index forecasting. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 12(11), 1793–1811 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Economics and ManagementTaiyuan University of TechnologyTaiyuanChina

Personalised recommendations