Abstract
The regression model is one of typical model for predicting some values by analyzing existing numerical data collected in various ways. If data are not crisp numbers, they are usually transformed into numerical crisp values by means of some methods such as quantification method. In companies’ decision making process, collected and referred data usually have uncertainty which sometimes play an important roles for business performance. One of authors have proposed a model deriving predicting values with uncertainty by handling data with uncertainties. In this paper, we review some method for this purpose, then describe the model by applying it to some test cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alim, A., Johora, F.T., Babu, S., Sultana, A.: Operations, elementary,on L-R fuzzy number. Adv. Pure Math. 5, 131–136 (2015)
Amagasa, M.: Formulation of a sale price prediction model based on fuzzy regression analysis. In: Klatte, D., Lüthi, H.J. (eds.) Operations Research Proceedings 2011. Operations Research Proceedings, pp. 567–572. Springer, Heidelberg (2012)
Diamond, P.: Fuzzy least squares. Inf. Sci. 46(3), 141–157 (1988)
D’Urso, P.: Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data. Comput. Stat. Data Anal. 42, 47–72 (2003)
Inoue, Y., Uematsu, Y., Amagasa, M., Tomizawa, G.: The method of setting forecast selling price model by fuzzy regression analysis. In: Proceedings of Japan industrial Management Association, Autumn Congress, pp. 194–195 (1991)
Kim, K.J., Moskowitz, H., Koksalan, M.: Fuzzy versus statistical linear regression. Eur. J. Oper. Res. 92(2), 417–434 (1996)
Guo, P., Tanaka, H.: Dual models for possibilistic regression analysis. Comput. Stat. Data Anal. 51(1), 252–266 (2006)
Gale, B.T.: Managing Customer Value. Free Press, New York (1994)
Peters, G.: Fuzzy linear regression with fuzzy intervals. Fuzzy Sets Syst. 63(1), 45–55 (1994)
Souhir, C., Karim, Z., Felix, M.-C.: Fuzzy regression analysis using trapezoidal fuzzy numbers. In: Proceedings of the Joint 4th Conference of the European Society for Fuzzy Logic and Technology and 11th Rencontres Francophones sur la logique Floue et ses Applications, pp. 1213–1218 (2005)
Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Syst. Man Cybern. SMC-12 6, 903–907 (1982)
Tanaka, H.: Fuzzy analysis by possibility linear models. Fuzzy Sets Syst. 24, 363–375 (1987)
Tanaka, H., Entani, T., Sugihara, K.: Approach to Interval Evaluation in Decision Making. J. Jpn. Soc. Fuzzy Theory Intell. Inf. 17(4), 406–412 (2005)
Shapiro, A.F.: Fuzzy Regression Models. ARC2005: Actuarial Research Clearing House 2006 (2005)
Wang, H.F., Tsaur, R.C.: Insight of a fuzzy regression model. Fuzzy Sets Syst. 112(3), 355–369 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Amagasa, M., Nagata, K. (2016). Prediction Model with Interval Data -Toward Practical Applications-. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-40581-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40580-3
Online ISBN: 978-3-319-40581-0
eBook Packages: Computer ScienceComputer Science (R0)