Skip to main content
SpringerLink
Log in

A Novel Moving Average Forecasting Approach Using Fuzzy Time Series Data Set

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

In this study, we develop a novel moving average forecasting approach based on fuzzy time series data set. The main objective of applying this moving average approach in develop method is to provide better results and enhance the accuracy in forecasted output by reducing the fluctuation in time series data set. The developed method is to define the universe of discourse and partition into equal length of intervals which is based on the average-length method. Further, triangular fuzzy sets are defined and obtain a membership grade of each moving average historical datum rather than actual datum of historical fuzzy time series data. Here, the fuzzification process of moving average historical data to their maximum membership grades obtained into corresponding triangular fuzzy sets. The general suitability of developed model has been examined by implementing in the forecast of student enrollments data at the University of Alabama. Further, the market price of State Bank of India share at Bombay Stock Exchange, India, has also been implemented in the forecast. The developed method of moving average fuzzy time series provides an improved forecasted output with least root mean square error and average forecasting errors which shows that our developed method is more superior than other existing models available in the literature based on fuzzy time series data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abhishekh, Bharati, S. K., & Singh, S. R. (2019). A novel approach to handle forecasting problems based on moving average two-factor fuzzy time series. In J. C. Bansal, K. N. Das, A. Nagar, K. Deep, & A. K. Ojha Soft computing for problem solving; Advances in intelligent systems and computing (Vol. 816). Singapore: Springer.

    Google Scholar 

  • Abhishekh, Gautam, S. S., & Singh, S. R. (2017). A refined weighted for forecasting based on type 2 fuzzy time series. International Journal of Modelling and Simulation, 38, 180–188.

    Article  Google Scholar 

  • Abhishekh, Gautam, S. S., & Singh, S. R. (2018a). A score function based method of forecasting using intuitionistic fuzzy time series. New Mathematics and Natural Computation, 14(1), 91–111.

    Article  MathSciNet  Google Scholar 

  • Abhishekh, Gautam, S. S., & Singh, S. R. (2018b). A refined method of forecasting based on high-order intuitionistic fuzzy time series data. Progress in Artificial Intelligence, 7(4), 339–350.

    Article  Google Scholar 

  • Abhishekh, & Kumar, S. (2017). A computational method for rice production forecasting based on high-order fuzzy time series. International Journal of Fuzzy Mathematical Archive, 13(2), 145–157.

    Google Scholar 

  • Aladag, C. H., Basaran, M. A., Egrioglu, E., Yolcu, U., & Uslu, V. R. (2009). Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations. Expert Systems with Applications, 36, 4228–4231.

    Article  Google Scholar 

  • Aladag, C. H., Yolcu, U., Egrioglu, E., & Dalar, A. Z. (2012). A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Applied Soft Computing, 12(10), 3291–3299.

    Article  Google Scholar 

  • Bisht, K., & Kumar, S. (2016). Fuzzy time series forecasting method based on hesitant fuzzy sets. Expert Systems with Applications, 64, 557–568.

    Article  Google Scholar 

  • Chang, X. H., Li, Z. M., & Park, J. H. (2017a). Fuzzy generalized H2 filtering for nonlinear discrete-time systems with measurements quantization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 99, 1–12.

    Google Scholar 

  • Chang, X. H., Park, J. H., & Shi, P. (2017b). Fuzzy resilient energy-to-peak filtering for continuous-time nonlinear systems. IEEE Transactions on Fuzzy Systems, 25(6), 1576–1588.

    Article  Google Scholar 

  • Chang, X. H., & Wang, Y. M. (2018). Peak to peak filtering for networked nonlinear DC motor systems with quantization. IEEE Transactions on Industrial Informatics, 14(12), 5378–5388.

    Article  Google Scholar 

  • Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311–319.

    Article  MathSciNet  Google Scholar 

  • Chen, S. M. (2002). Forecasting enrollments based on high-order fuzzy time series. Cybernetics and Systems, 33(1), 1–16.

    Article  MATH  Google Scholar 

  • Chen, S. M., & Chung, N. Y. (2006). Forecasting enrollments using high-order fuzzy time series and genetic algorithms. International Journal of Intelligent Systems, 21(5), 485–501.

    Article  MATH  Google Scholar 

  • Chen, S. M., & Hsu, C. C. (2004). A new method to forecast enrollments using fuzzy time series. International Journal of Applied Science and Engineering, 2(3), 234–244.

    Google Scholar 

  • Chen, S. M., & Kao, P. Y. (2013). TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Information Sciences, 247, 62–71.

    Article  MathSciNet  Google Scholar 

  • Chen, S. M., & Tanuwijaya, K. (2011). Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Systems with Applications, 38, 15425–15437.

    Article  Google Scholar 

  • Chen, S. M., Wang, N. Y., & Pan, J. S. (2009). Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships. Expert Systems with Applications, 36(8), 11070–11076.

    Article  Google Scholar 

  • Eǧrioǧlu, E. (2012). A new time-invariant fuzzy time series forecasting method based on genetic algorithm. Advances in Fuzzy Systems, 2012, 785709. https://doi.org/10.1155/2012/785709.

    Article  MathSciNet  MATH  Google Scholar 

  • Fraccaroli, F., Peruffo, A., & Zorzi, M. A. (2015). A new recursive least-squares method with multiple forgetting schemes. In: 2015 54th IEEE conference on decision and control (CDC) (pp. 3367–3372).

  • Gangwar, S. S., & Kumar, S. (2012). Partitions based computational method for high-order fuzzy time series forecasting. Expert Systems with Applications, 39(15), 12158–12164.

    Article  Google Scholar 

  • Gangwar, S. S., & Kumar, S. (2014). Probabilistic and intuitionistic fuzzy sets-based method for fuzzy time series forecasting. Cybernetics and Systems, 45(4), 349–361.

    Article  MATH  Google Scholar 

  • Gangwar, S. S., & Kumar, S. (2015). Computational method for high-order weighted fuzzy time series forecasting based on multiple partitions. In M. Chakraborty, A. Skowron, M. Maiti, & S. Kar (Eds.), Facets of uncertainties and applications (pp. 293–302). New Delhi: Springer.

    Chapter  Google Scholar 

  • Gautam, S. S., Abhishekh, & Singh, S. R. (2018a). An improved-based TOPSIS method in interval valued intuitionistic fuzzy environment. Life Cycle Reliability and Safety Engineering, 7, 81–88.

    Article  Google Scholar 

  • Gautam, S. S., Abhishekh, & Singh, S. R. (2018b). An intuitionistic fuzzy soft set theoretic approach to decisions making problems. MATEMATIKA, 34, 49–58.

    Article  Google Scholar 

  • Gautam, S. S., Abhishekh, & Singh, S. R. (2018c). A new high-order approach for forecasting fuzzy time series data. International Journal of Computational Intelligence and Applications, 17, 1850019.

    Article  Google Scholar 

  • Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, 123(3), 387–394.

    Article  MathSciNet  MATH  Google Scholar 

  • Huarng, K., & Yu, T. H. (2006). Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36(2), 328–340.

    Article  Google Scholar 

  • Huo, X., Ma, L., Zhao, X., & Zong, G. (2019). Observer-based fuzzy adaptive stabilization of uncertain switched stochastic nonlinear systems with input quantization. Journal of the Franklin Institute, 356, 1789–1809.

    Article  MathSciNet  MATH  Google Scholar 

  • Hwang, J. R., Chen, S. M., & Lee, C. H. (1998). Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems, 100(1–3), 217–228.

    Article  Google Scholar 

  • Jilani, T. A., & Burney, S. M. (2008). Multivariate stochastic fuzzy forecasting models. Expert Systems with Applications, 35(3), 691–700.

    Article  Google Scholar 

  • Lee, H. S., & Chou, M. T. (2004). Fuzzy forecasting based on fuzzy time series. International Journal of Computer Mathematics, 81(7), 781–789.

    Article  MathSciNet  MATH  Google Scholar 

  • Lee, L. W., Wang, L. H., Chen, S. M., & Leu, Y. H. (2006). Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14(3), 468–477.

    Article  Google Scholar 

  • Li, S. T., & Cheng, Y. C. (2007). Deterministic fuzzy time series model for forecasting enrollments. Computers & Mathematics with Applications, 53(12), 1904–1920.

    Article  MathSciNet  MATH  Google Scholar 

  • Pathak, H. K., & Singh, P. (2011). A new bandwidth interval based forecasting method for enrollments using fuzzy time series. Applied Mathematics, 2(04), 504.

    Article  Google Scholar 

  • Qiu, W., Liu, X., & Li, H. (2011). A generalized method for forecasting based on fuzzy time series. Expert Systems with Applications, 38(8), 10446–10453.

    Article  Google Scholar 

  • Singh, S. R. (2007a). A simple method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 186(1), 330–339.

    Article  MathSciNet  MATH  Google Scholar 

  • Singh, S. R. (2007b). A robust method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 188(1), 472–484.

    Article  MathSciNet  MATH  Google Scholar 

  • Song, Q. (2003). A note on fuzzy time series model selection with sample autocorrelation functions. Cybernetics & Systems, 34(2), 93–107.

    Article  MATH  Google Scholar 

  • Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets and Systems, 54(1), 1–9.

    Article  Google Scholar 

  • Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets and Systems, 62(1), 1–8.

    Article  Google Scholar 

  • Wang, N. Y., & Chen, S. M. (2009). Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series. Expert Systems with Applications, 36(2), 2143–2154.

    Article  Google Scholar 

  • Wang, Y., Lei, Y., Fan, X., & Wang, Y. (2016). Intuitionistic fuzzy time series forecasting model based on intuitionistic fuzzy reasoning. Mathematical Problems in Engineering, 2016, 5035160. https://doi.org/10.1155/2016/5035160.

    Article  MathSciNet  MATH  Google Scholar 

  • Wong, W. K., Bai, E., & Chu, A. W. (2010). Adaptive time-variant models for fuzzy time series forecasting. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(6), 1531–1542.

    Article  Google Scholar 

  • Yolcu, U., Egrioglu, E., Uslu, V. R., Basaran, M. A., & Aladag, C. H. (2009). A new approach for determining the length of intervals for fuzzy time series. Applied Soft Computing, 9(2), 647–651.

    Article  MATH  Google Scholar 

  • Yu, H. K. (2005). A refined fuzzy time-series model for forecasting. Physica A: Statistical Mechanics and Its Applications, 346(3), 657–681.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhao, X., Shi, P., & Zheng, X. (2016). Fuzzy adaptive control design and discretization for a class of nonlinear uncertain systems. IEEE Transactions on Cybernetics, 46(6), 1476–1483.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are very thankful to the editor and the anonymous reviewers for their constructive suggestions and comments to enhance the quality of the study.

Author information

Authors and Affiliations

  1. Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi, 221005, India

    Surendra Singh Gautam &  Abhishekh

Authors
  1. Surendra Singh Gautam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhishekh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishekh.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gautam, S.S., Abhishekh A Novel Moving Average Forecasting Approach Using Fuzzy Time Series Data Set. J Control Autom Electr Syst 30, 532–544 (2019). https://doi.org/10.1007/s40313-019-00467-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-019-00467-w

Keywords

Search

Navigation