Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach
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ORIGINAL ARTICLE
Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach Tin-Chih Toly Chen1 · Hsin-Chieh Wu2 Received: 10 February 2020 / Accepted: 25 April 2020 © The Author(s) 2020
Abstract A layered partial-consensus fuzzy collaborative forecasting approach is proposed in this study to forecast the unit cost of a dynamic random access memory (DRAM) product. In the layered partial-consensus fuzzy collaborative forecasting approach, the partial-consensus fuzzy intersection (PCFI) operator is applied instead of the prevalent fuzzy intersection (FI) operator to aggregate the fuzzy forecasts by experts. In this way, some meaningful information, such as the suitable number of experts, can be obtained through observing changes in the PCFI result when the number of experts varies. After applying the layered partial-consensus fuzzy collaborative forecasting approach to a real case, the experimental results revealed that the layered partial-consensus fuzzy collaborative forecasting approach outperformed three existing methods. The most significant advantage was up to 13%. Keywords Fuzzy collaborative forecasting · Dynamic random access memory · Layered partial consensus
Introduction Fuzzy collaborative forecasting is the combination of fuzzy forecasting [28] and collaborative intelligence [18, 25]. In a fuzzy collaborative forecasting approach, multiple experts apply various fuzzy forecasting methods to forecast a target and collaborate by consulting each other’s forecast so as to modify their fuzzy forecasting methods or forecasts [13]. Unlike conventional forecasting methods that are focused on optimizing the forecasting accuracy, a fuzzy collaborative forecasting approach attempts to optimize both the forecasting precision and accuracy [12]. In this study, the unit cost of a dynamic random access memory (DRAM) product is to be forecasted. The unit cost of a DRAM product is a special time series [11]. For this reason, some recent references on fuzzy time series forecasting are reviewed as follows. To forecast the weighted stock index, Wong et al. [43] applied fuzzy inference rules.
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Hsin-Chieh Wu [email protected]
1
Department of Industrial Engineering and Management, National Chiao Tung University, University Road, 1001 Hsinchu, Taiwan
2
Department of Industrial Engineering and Management, Chaoyang University of Technology, No.168, Jifeng E. Rd, Wufeng District, Taichung City 41349, Taiwan
According to the forecasting accuracy, the input space was redivided by adjusting the window size. Egrioglu et al. [21] applied fuzzy c-means (FCM) and an artificial neural network (ANN) jointly to forecast the enrollment result of University of Alabama. First, FCM was applied to fuzzify historical data. Subsequently, the fuzzification results became inputs to an ANN that forecasted the enrollment result. Cai et al. [4] established a fuzzy autoregression model to forecast the weighted stock index. Ant colony optimization (ACO) was applied to optimize the fuzzi
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