[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

Forecasting analysis by using fuzzy grey regression model for solving limited time series data

  • Original Paper
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The grey model GM(1,1) is a popular forecasting method when using limited time series data and is successfully applied to management and engineering applications. On the other hand, the reliability and validity of the grey model GM(1,1) have never been discussed. First, without considering other causes when using limited time series data, the forecasting of the grey model GM(1,1) is unreliable, and provide insufficient information to a decision maker. Therefore, for the sake of reliability, the fuzzy set theory was hybridized into the grey model GM(1,1). This resulted in the fuzzy grey regression model, which granulates a concept into a set with membership function, thereby obtaining a possible interval extrapolation. Second, for a newly developed product or a newly developed system, the data collected are limited and rather vague with the result that the grey model GM(1,1) is useless for solving its problem with vague or fuzzy-input values. In this paper the fuzzy grey regression model is verified to show its validity in solving crisp-input data and fuzzy-input data with limited time series data. Finally, two examples for the LCD TV demand are illustrated using the proposed models.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abdalla A and Buckley JJ (2007). Monte Carlo methods in fuzzy linear regression. Soft Computing 11(10): 991–996

    Article  MATH  Google Scholar 

  • Bardossy A, Bogardi I and Duckstein L (1990). Fuzzy Regression in Hydrology. Water Resources Research 26(7): 1497–1508

    Google Scholar 

  • Chang PT (1997). Fuzzy seasonality forecasting. Fuzzy Sets and Systems 90(1): 1–10

    Article  Google Scholar 

  • Chang PT, Lee ES and Konz SA (1996). Applying fuzzy linear regression to VDT legibility. Fuzzy Sets and Systems 80(2): 197–204

    Article  Google Scholar 

  • Chang SC (2005). The TFT–LCD industry in Taiwan: competitive advantages and future developments. Technology in Society 27(2): 199–215

    Article  Google Scholar 

  • Cheng CB (2005). Fuzzy process control: construction of control charts with fuzzy numbers. Fuzzy Sets and Systems 154(2): 287–303

    Article  MathSciNet  Google Scholar 

  • Chiao CH and Wang WY (2002). Reliability improvement of fluorescent lamp using grey forecasting model. Microelectronics Reliability 42(1): 127–134

    Article  Google Scholar 

  • Deng LJ (1986). Grey forecasting and decision. Huazhong of Society and Technology Press, Wuhan

    Google Scholar 

  • Deng LJ (1989). Introduction to grey system theory. The Journal of Grey System 1(1): 1–24

    MATH  MathSciNet  Google Scholar 

  • Funga RYK, Chen Y and Tang J (2006). Estimating the functional relationships for quality function deployment under uncertainties. Fuzzy Sets and Systems 157(1): 98–120

    Article  MathSciNet  Google Scholar 

  • Hsu CI and Wen YH (2000). Application of grey theory and mutiobjective programming towards airline network design. European Journal of Operational Research 127(1): 44–68

    Article  MATH  Google Scholar 

  • Hsu CC and Chen CY (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management 44(14): 2241–2249

    Article  Google Scholar 

  • Hsu LC (2003). Applying the grey prediction model to the global integrated circuit industry. Technological Forecasting & Social Change 70(6): 563–574

    Article  Google Scholar 

  • Kim KJ, Moskowitz H and Koksalan M (1996). Fuzzy versus statistical linear regression. European Journal of Operational Research 92(2): 417–434

    Article  MATH  Google Scholar 

  • Lin CT and Yang SY (2003). Forecast of the output value if Taiwan’s opto-electronics industry using the grey forecasting model. Technological Forecasting & Social Change 70(2): 177–186

    Article  Google Scholar 

  • Menozzi M, Napflin U and Krueger H (1999). CRT versus LCD: A pilot study on visual performance and suitability of two display technologies for use in office work. Displays 20(1): 3–10

    Article  Google Scholar 

  • Moskowitz H and Kim K (1993). On assessing the h value in Fuzzy Linear Regression. Fuzzy Sets and Systems 58(3): 303–327

    Article  MATH  MathSciNet  Google Scholar 

  • Tanaka H, Uejima S and Asai K (1982). Fuzzy linear regression model. IEEE Transaction on SMC 12(6): 903–907

    MATH  Google Scholar 

  • Tsaur RC, Wang HF and O Yang JC (2002). Fuzzy Regression for Seasonal Time Series Analysis. International Journal of Information Technology & Decision Making 1(1): 165–175

    Article  Google Scholar 

  • Tsaur RC (2003). Extrapolating internet users in Taiwan by risk assessment. Computers and Mathematics with Applications 46(10–11): 1725–1734

    Article  MATH  Google Scholar 

  • Tseng FM, Tzeng GH, Yu HC and Yuan Benjamin JC (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets and Systems 118(1): 9–19

    Article  MathSciNet  Google Scholar 

  • Watada J (1992) Fuzzy time series analysis and forecasting of sales volume. In: Kacprzyk J, Fedrizzi M (eds.), Fuzzy Regression Analysis, Omnitech Press, Warsaw and Physica-Verlag, Heidelberg 211–227

  • Zadeh LA (1965). Fuzzy sets, Inform. Control l. 8(3): 338–353

    Article  MATH  MathSciNet  Google Scholar 

  • Optotech, Chinese Information Co. LTD, Taipei, n.74 (2005) (In Chinese)

  • Topology research institute, Open a new world of LCD : Trend Analysis in LCD TV market, Topology, Taipei (2005) (In Chinese)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruey-Chyn Tsaur.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tsaur, RC. Forecasting analysis by using fuzzy grey regression model for solving limited time series data. Soft Comput 12, 1105–1113 (2008). https://doi.org/10.1007/s00500-008-0278-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-008-0278-z

Keywords

Navigation