Forecasting methods using fuzzy concepts

Tin-Chih Chen; Mao Jiun J. Wang

doi:10.1016/S0165-0114(97)00265-0

Forecasting methods using fuzzy concepts

Tin-Chih Chen^*, Mao Jiun J. Wang

^*Corresponding author for this work

Department of Industrial Engineering and Management

Research output: Contribution to journal › Article › peer-review

55 Scopus citations

Abstract

In this paper, fuzzy concepts are applied in forecasting product price and sales in the semiconductor industry which is often conceived as a highly dynamic environment. First, two fuzzy forecasting methods including fuzzy interpolation (FI) and fuzzy linear regression (FLR) are developed and discussed. Forecasts generated by these methods are fuzzy-valued. Next, the subjective beliefs about whether the industry is booming or slumping, and the speed at which this change in prosperity takes place during a given period are also considered. Two subjective functions are defined and used to adjust fuzzy forecasts. Practically, fuzzy forecasts are incorporated with fuzzy programming like fuzzy linear programming (FLP) or fuzzy nonlinear programming (FNP) for mid-term or long-term planning. Advantages over traditional methods are shown in our examples.

Original language	English
Pages (from-to)	339-352
Number of pages	14
Journal	Fuzzy Sets and Systems
Volume	105
Issue number	3
DOIs	https://doi.org/10.1016/S0165-0114(97)00265-0
State	Published - 1 Aug 1999

Keywords

Fuzzy forecasting
Fuzzy interpolation
Fuzzy linear programming
Fuzzy linear regression
Linguistic values
Semiconductor
Subjective functions

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AB - In this paper, fuzzy concepts are applied in forecasting product price and sales in the semiconductor industry which is often conceived as a highly dynamic environment. First, two fuzzy forecasting methods including fuzzy interpolation (FI) and fuzzy linear regression (FLR) are developed and discussed. Forecasts generated by these methods are fuzzy-valued. Next, the subjective beliefs about whether the industry is booming or slumping, and the speed at which this change in prosperity takes place during a given period are also considered. Two subjective functions are defined and used to adjust fuzzy forecasts. Practically, fuzzy forecasts are incorporated with fuzzy programming like fuzzy linear programming (FLP) or fuzzy nonlinear programming (FNP) for mid-term or long-term planning. Advantages over traditional methods are shown in our examples.

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KW - Fuzzy linear programming

KW - Fuzzy linear regression

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Forecasting methods using fuzzy concepts

Abstract

Keywords

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