Skip to main content
SpringerLink
Log in

A New Refined Forecasting Model Based on the High - Order Time-Variant Fuzzy Relationship Groups and Particle Swam Optimization

  • Conference paper
  • First Online:
Advances in Engineering Research and Application (ICERA 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 104))

Included in the following conference series:

Abstract

In this paper, a novel refined fuzzy time series (FTS) model is presented to handle three main issues, viz., determination of effective length of intervals, handling of fuzzy logical relationships (FLRs) and determination of forecasted output values. In forecasting models, lengths of intervals always affect the results of forecasting. So, we use particle swarm optimization (PSO) technique to find the optimal length of intervals in the universe of discourse. Most of the existing forecasting models simply ignore the repeated FLRs without any proper justification or accept the number of recurrence of the FLRs without considering the appearance history of these fuzzy sets in the grouping fuzzy logical relationships process. Therefore, in this study, we consider the appearance history of the fuzzy sets on the right-hand side of the FLRs to establish the fuzzy logical relationship groups, called the time - variant fuzzy relationship groups (TV-FRGs). Furthermore, many researchers suggest that high-order FLRs improve the forecasting accuracy of the models. Therefore, we also use the high-order fuzzy relationships in order to obtain more accurate forecasting results. Proposed model is applied to forecast two numerical datasets (enrollments data of the University of Alabama, dataset of Gasonline Price in Viet Nam). The results indicate that the proposed model gets a higher average forecasting accuracy rate to forecast enrolments of the University of Alabama than the existing methods based on the high-order FTS.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bose, M., Mali, K.: A novel data partitioning and rule selection technique for modelling high – order fuzzy time series. Appl. Soft Comput. (2017). https://doi.org/10.1016/j.asoc.2017.11.011

    Article  Google Scholar 

  2. Chen, S.M.: Forecasting enrolments based on fuzzy time series. Fuzzy Set Syst. 81, 311–319 (1996)

    Article  Google Scholar 

  3. Chen, S.M.: Forecasting enrolments based on high-order fuzzy time series. Cybern. Syst. 33(1), 1–16 (2002)

    Article  Google Scholar 

  4. Chen, S.M., Phuong, B.D.H.: Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowl.-Based Syst. 118, 204–216 (2017)

    Article  Google Scholar 

  5. Chen, S.M., Chung, N.Y.: Forecasting enrolments using high-order fuzzy time series and genetic algorithms. Int. J. Intell. Syst. 21, 485–501 (2006)

    Article  Google Scholar 

  6. Chen, S.M., Tanuwijaya, K.: Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Syst. Appl. 38, 15425–15437 (2011)

    Article  Google Scholar 

  7. Chen, S.-M., Jian, W.-S.: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Inf. Sci. 391–392, 65–79 (2017)

    Article  Google Scholar 

  8. Cheng, S.H., Chen, S.-M., Jian, W.S.: Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf. Sci. 327, 272–287 (2016)

    Article  MathSciNet  Google Scholar 

  9. Egrioglu, E., Aladag, C.H., Yolcu, U.: Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst. Appl. 40(3), 854–857 (2013)

    Article  Google Scholar 

  10. Huang, Y.L., et al.: A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization. Expert Syst. Appl. 7(38), 8014–8023 (2011)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst. 100, 217–228 (1998)

    Article  Google Scholar 

  13. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, 4, Perth, Australia, pp. 1942–1948 (1995)

    Google Scholar 

  14. Kuo, I.-H., et al.: An improved method for forecasting enrolments based on fuzzy time series and particle swarm optimization. Expert Syst. Appl. 36, 6108–6117 (2009)

    Article  Google Scholar 

  15. Kuo, I.-H., et al.: Forecasting TAIFEX based on fuzzy time series and particle swarm optimization. Expert Syst. Appl. 2(37), 1494–1502 (2010)

    Article  Google Scholar 

  16. Lee, L.-W., Wang, L.-H., Chen, S.-M.: Temperature prediction and TAIFEX forecasting based on high order fuzzy logical relationship and genetic simulated annealing techniques. Expert Syst. Appl. 34, 328–336 (2008)

    Article  Google Scholar 

  17. Lee, L.W., et al.: Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Trans. Fuzzy Syst. 14, 468–477 (2006)

    Article  Google Scholar 

  18. Hsu, L.-Y., et al.: Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Syst. Appl. 37, 2756–2770 (2010)

    Article  Google Scholar 

  19. Dieu, N.C., Van Tinh, N.: Fuzzy time series forecasting based on time-depending fuzzy relationship groups and particle swarm optimization. In: Proceedings of the 9th National conference on Fundamental and Applied Information Technology Research (FAIR’9), pp. 125–133 (2016)

    Google Scholar 

  20. Van Tinh, N., Dieu, N.C.: An improved method for stock market forecasting combining high-order time variant fuzzy logical relationship groups and particle swam optimization. In: Proceedings of the International Conference, ICTA, pp. 153–166 (2016)

    Google Scholar 

  21. Park, J.I., Lee, D.J., Song, C.K., Chun, M.G.: TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization. Expert Syst. Appl. 37, 959–967 (2010)

    Article  Google Scholar 

  22. Singh, S.R.: A simple method of forecasting based on fuzzy time series. Appl. Math. Comput. 186, 330–339 (2007)

    MathSciNet  MATH  Google Scholar 

  23. Song, Q., Chissom, B.S.: Forecasting enrolments with fuzzy time series - Part I. Fuzzy Sets Syst. 54(1), 1–9 (1993)

    Article  Google Scholar 

  24. Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets Syst. 54(3), 269–277 (1993)

    Article  MathSciNet  Google Scholar 

  25. Tian, Z.H., Wang, P., He, T.Y.: Fuzzy time series based on K-means and particle swarm optimization algorithm. In: Man-Machine-Environment System Engineering. Lecture Note in Electrical Engineering, vol. 406, pp. 181–189. Springer (2016)

    Google Scholar 

  26. Tinh, N.V., Dieu, N.C.: A novel forecasting model based on combining time-variant fuzzy logical relationship groups and K-means clustering technique. In: Proceedings of the 9th National Conference on Fundamental and Applied Information Technology Research (FAIR10) (2017). https://doi.org/10.15625/vap.2017.0002

  27. Yu, H.K.: A refined fuzzy time-series model for forecasting. Phys. A Stat. Mech. Appl. 346(3-4), 657–681 (2005)

    Article  Google Scholar 

  28. Yu, H.K.: Weighted fuzzy time series models for TAIFEX forecasting. Phys. A Stat. Mech. Appl. 349(3–4), 609–624 (2005)

    Article  Google Scholar 

  29. Wang, H., Guo, J., Feng, H., Jin, H.: An improved forecasting model of fuzzy time series. Appl. Mech. Mater. 678, 64–69 (2014). [3rd International Conference on Mechatronics and Control Engineering (ICMCE 2014)]

    Article  Google Scholar 

  30. Xu, W.H., Guo, J.C., Feng, H., Jin, H.L.: A fuzzy time series forecasting model based on data differences. 1 ICT IN EDUCATION. Frontiers in Computer Education, pp. 15–18 (2014). 2nd International Conference on Frontiers in Computer Education (ICFCE 2014), Wuhan, China (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Thai Nguyen University of Technology, Thai Nguyen University, Thai Nguyen, Vietnam

    Nghiem Van Tinh

Authors
  1. Nghiem Van Tinh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nghiem Van Tinh .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Automation, TU Ilmenau, Ilmenau, Thüringen, Germany

    Kai-Uwe Sattler

  2. Faculty of Electronic Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Duy Cuong Nguyen

  3. Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Ngoc Pi Vu

  4. Hanoi University of Science and Technology, Hanoi, Vietnam

    Banh Tien Long

  5. Institute for Automation and Systems Engineering, TU Ilmenau, Ilmenau, Thüringen, Germany

    Horst Puta

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Van Tinh, N. (2020). A New Refined Forecasting Model Based on the High - Order Time-Variant Fuzzy Relationship Groups and Particle Swam Optimization. In: Sattler, KU., Nguyen, D., Vu, N., Tien Long, B., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2019. Lecture Notes in Networks and Systems, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-030-37497-6_3

Download citation

Publish with us

Policies and ethics

Search

Navigation