Skip to main content
SpringerLink
Log in

Handling Seasonal Pattern and Prediction Using Fuzzy Time Series Model

  • Chapter
  • First Online:
Algorithms in Machine Learning Paradigms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 870))

  • 1521 Accesses

Abstract

Seasonal variation is one of the important components of the time series. There are many techniques available in the literature to deal with the problem of seasonality. A few hybrid fuzzy time series models investigated the problem of forecasting in the presence of seasonal variation. But these techniques follow complex computational procedures. The aim of this present study is to develop a new fuzzy time series forecasting model that can process seasonal patterns present in the data directly without any seasonal adjustment by applying certain mathematical techniques. The proposed Neuro-uzzy model is capable of extracting the seasonal pattern from the training set and forecasting the future pattern. This model makes use of Self-organizing map (SOM) for clustering similar patterns. Performance of the model is evaluated using Rainfall data and Milk Production data.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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. Hylleberg S (1992) Modeling seasonality. Oxford University Press, Oxford

    MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  3. Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets Syst 54:1–9

    Article  Google Scholar 

  4. Song Q, Chissom (1994) Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets Syst 64:1–8

    Google Scholar 

  5. Zadeh LA (1965) Fuzzy set. Inf Control 8:338–353

    Article  Google Scholar 

  6. Chen SM (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81:311–319

    Article  Google Scholar 

  7. Lee LW, Wang LH, Chen SM, Leu YH (2006) Handling fore casting problems based on two-factors high-order fuzzy time series. IEEE Trans Fuzzy Syst 14(3):468–477

    Article  Google Scholar 

  8. Liu H-T, Wei M-L (2010) An improved fuzzy forecasting method for sea sonal time series. Expert Syst Appl 37(9):6310–6318

    Article  Google Scholar 

  9. Chen S-M, Phuong BDH (2017) Fuzzy time series forecasting based on op timal partitions of intervals and optimal weighting vectors. Knowl-Based Syst 118:204–216

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  12. Cai Q, Zhang D, Zheng W, Leung SCH (2015) A new fuzzy time series fo recasting model combined with ant colony optimization and auto-regression. Knowl-Based Syst 74:61–68

    Article  Google Scholar 

  13. Chen MY (2014) A high-order fuzzy time series forecasting model for inter net stock trading. Future Gener Comput System 37:461–467

    Article  Google Scholar 

  14. Bose M, Mali K (2018) An improved technique for modeling fuzzy time series. In: the Proceedings of 2nd International Conference on Computational Intelligence, Communications, and Business Analytics, Kalyani Govt. Engg. College, West Bengal (Communications in Computer and Information Science, Vol. 1030, Springer, 2019)

    Google Scholar 

  15. Bisht K, Kumar S (2016) Fuzzy time series forecasting method based on he sitant fuzzy sets. Expert Syst Appl 64:557–568

    Google Scholar 

  16. Bose M, Mali K (2018) A novel data partitioning and rule selection tech nique for modeling high-order fuzzy time series. Appl Soft Comput 63:87–96

    Article  Google Scholar 

  17. Rubio A, Bermúdez J, Vercher E (2017) Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Syst Appl 76:12–20

    Article  Google Scholar 

  18. Deng W, Wang G, Zhang X, Xu J, Li G (2016) A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques. Neurocomputing 173:1671–1682

    Article  Google Scholar 

  19. Wang W, Pedrycz W, Liu X (2015) Time series long-term forecasting model based on information granules and fuzzy clustering. Eng Appl Artif Intell 41:17–24

    Google Scholar 

  20. Singh P (2016) Rainfall and financial forecasting using fuzzy time series and neural networks based model. Int J Mach Learn Cyber https://doi.org/10.1007/s13042-016-0548-5

  21. Hsu L-Y, Horng S-J, Kao T-W, Chen Y-H, Run R-S, Chen R-J, Lai J-L, Kuo I-H (2010) Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Syst Appl 37:2756–2770

    Article  Google Scholar 

  22. Singh P, Borah B (2013) High-order fuzzy-neuro expert system for daily tem perature Forecasting. Knowl-Based Syst 46:12–21

    Article  Google Scholar 

  23. Bose M, Mali, K (2017) Fuzzy time series forecasting model using particle swarm optimization and neural network. In: the Proceedings of 7th International Conference. Soft Computing for Problem Solving, IIT, Bhubaneswar, Odisha (Advances in Intelligent Systems and Computing, Vol. 816, Springer, 2019)

    Google Scholar 

  24. Efendi R, Ismail Z, Deris MM (2015) A new linguistic out-sample ap proach of fuzzytime series for daily forecasting of Malaysian electricity load demand. Appl Soft Comput J 28:422–430

    Article  Google Scholar 

  25. Sadaei HJ, Guimarães FG, da Silva CJ, Lee MH, Eslami T (2017) Short-term load forecasting method based on fuzzy time series, seaso nality and long memory process. Int J Approx Reson 83:196–217

    Article  Google Scholar 

  26. Domanska D, Wojtylak M (2012) Application of fuzzy time series models for forecasting pollution concentrations. Expert Syst Appl 39(9):7673–7679

    Google Scholar 

  27. Song Q (1999) Seasonal forecasting in fuzzy time series. Fuzzy Sets Syst 107:235–236

    Article  MathSciNet  Google Scholar 

  28. Chang P-T (1997) Fuzzy seasonality forecasting. Fuzzy Sets Syst 90:l–10

    Google Scholar 

  29. Tseng F-M, Tzeng G-H (2002) A fuzzy seasonal ARIMA model for fore casting. Fuzzy Sets Syst 126:367–376

    Article  Google Scholar 

  30. Mansfield E (1994) Statistics for business and economics: Methods and application. NY: W.W. Norton and Company

    Google Scholar 

  31. Egrioglu E, Aladag CH, Yolcu U, Basaran MA, Uslu VR (2009) A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Syst Appl 36:7424–7434

    Article  Google Scholar 

  32. Bulut E (2014) Modeling seasonality using the fuzzy integrated logical fore casting (FILF) approach. Expert Syst Appl 41(4 PART 2):1806–1812

    Google Scholar 

  33. Nguyen L, Novák V (2019) Forecasting seasonal time series based on fuzzy techniques. Fuzzy Sets Syst 361:114–129

    Google Scholar 

  34. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Oakland, CA

    MATH  Google Scholar 

  35. Kohonen Teuvo (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69. https://doi.org/10.1007/bf00337288

    Article  MATH  Google Scholar 

  36. Yang X, Yu F, Pedrycz W (2017) Long-term forecasting of time series based on linear fuzzy information granules and fuzzy inference system. Int J Approx Reason 81:1–27

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, University of Kalyani, Nadia, West Bengal, India

    Mahua Bose & Kalyani Mali

Authors
  1. Mahua Bose
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kalyani Mali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahua Bose .

Editor information

Editors and Affiliations

  1. Kalyani University, Kalyani, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Triguna Sen School of Technology, Assam University, Silchar, Assam, India

    Somnath Mukhopadhyay

  3. Department of Computer and System Sciences, Visva-Bharati University, Bolpur, West Bengal, India

    Paramartha Dutta

  4. Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India

    Kousik Dasgupta

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bose, M., Mali, K. (2020). Handling Seasonal Pattern and Prediction Using Fuzzy Time Series Model. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Algorithms in Machine Learning Paradigms. Studies in Computational Intelligence, vol 870. Springer, Singapore. https://doi.org/10.1007/978-981-15-1041-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1041-0_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1040-3

  • Online ISBN: 978-981-15-1041-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation