Skip to main content

Linear Fuzzy Collaborative Forecasting Methods

  • Chapter
  • First Online:
Fuzzy Collaborative Forecasting and Clustering

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

Linear methods have been widely applied to forecasting. Prevalent linear forecasting methods include moving average, exponential smoothing, linear regression (LR), autoregressive integrated moving average (ARIMA), and others. Fuzzifying the parameters of a linear forecasting method changes it to a linear fuzzy forecasting method.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. K.B. Song, Y.S. Baek, D.H. Hong, G. Jang, Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Trans. Power Syst. 20(1), 96–101 (2005)

    Article  Google Scholar 

  2. J. Watada, H. Tanaka, T. Shimomura, Identification of learning curve based on possibilistic concepts. Adv. Human Factors/Ergon. 6, 191–208 (1986)

    Google Scholar 

  3. T. Chen, M.J. Wang, A fuzzy set approach for yield learning modeling in wafer manufacturing. IEEE Trans. Semicond. Manuf. 12(2), 252–258 (1999)

    Article  Google Scholar 

  4. F.M. Tseng, G.H. Tzeng, H.C. Yu, B.J. Yuan, Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets Syst. 118(1), 9–19 (2001)

    Article  MathSciNet  Google Scholar 

  5. T. Chen, Y.C. Wang, An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting. IEEE Trans. Fuzzy Syst. 22(1), 201–211 (2014)

    Article  Google Scholar 

  6. H. Tanaka, J. Watada, Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst. 27(3), 275–289 (1988)

    Article  MathSciNet  Google Scholar 

  7. G. Peters, Fuzzy linear regression with fuzzy intervals. Fuzzy Sets Syst. 63(1), 45–55 (1994)

    Article  MathSciNet  Google Scholar 

  8. S. Donoso, N. Marin, M.A. Vila, Quadratic programming models for fuzzy regression, in Proceedings of International Conference on Mathematical and Statistical Modeling in Honor of Enrique Castillo (2006)

    Google Scholar 

  9. T. Chen, Y.C. Lin, A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting. Int. J. Uncertainty Fuzziness Knowledge-Based Syst. 16(01), 35–58 (2008)

    Article  Google Scholar 

  10. T. Chen, An innovative fuzzy and artificial neural network approach for forecasting yield under an uncertain learning environment. J. Ambient Intell. Humanized Comput. (2018)

    Google Scholar 

  11. J. Nocedal, S. Wright, Numerical Optimization (Springer Science & Business Media, New York, 2006)

    MATH  Google Scholar 

  12. I.S. Cheng, Y. Tsujimura, M. Gen, T. Tozawa, An efficient approach for large scale project planning based on fuzzy Delphi method. Fuzzy Sets Syst. 76, 277–288 (1995)

    Article  Google Scholar 

  13. A. Maturo, A.G.S. Ventre, Models for consensus in multiperson decision making, in 2008 Annual Meeting of the North American Fuzzy Information Processing Society (2008), pp. 1–4

    Google Scholar 

  14. T. Chen, An online collaborative semiconductor yield forecasting system. Expert Syst. Appl. 36(3), 5830–5843 (2009)

    Article  Google Scholar 

  15. L.I. Kuncheva, R. Krishnapuram, A fuzzy consensus aggregation operator. Fuzzy Sets Syst. 79, 347–356 (1996)

    Article  MathSciNet  Google Scholar 

  16. Y.C. Wang, T. Chen, A partial-consensus posterior-aggregation FAHP method—supplier selection problem as an example. Mathematics 7(2), 179 (2019)

    Article  Google Scholar 

  17. T. Chen, A collaborative fuzzy-neural system for global CO2 concentration forecasting. Int. J. Innov. Comput. Inf. Control 8(11), 7679–7696 (2012)

    Google Scholar 

  18. X. Liu, Parameterized defuzzification with maximum entropy weighting function—another view of the weighting function expectation method. Math. Comput. Model. 45, 177–188 (2007)

    Article  MathSciNet  Google Scholar 

  19. E. Eraslan, The estimation of product standard time by artificial neural networks in the molding industry. Math. Probl. Eng. article ID 527452 (2009)

    Google Scholar 

  20. A. Ranganathan, The Levenberg-Marquardt Algorithm (2004). Available: http://www.scribd.com/doc/10093320/Levenberg-Marquardt-Algorithm

  21. T. Chen, Forecasting the unit cost of a product with some linear fuzzy collaborative forecasting models. Algorithms 5(4), 449–468 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, TC.T., Honda, K. (2020). Linear Fuzzy Collaborative Forecasting Methods. In: Fuzzy Collaborative Forecasting and Clustering. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-22574-2_2

Download citation

Publish with us

Policies and ethics