Abstract
Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.
Highlights
Fuzzy collaborative forecasting combines fuzzy forecasting and collaborative intelligence [1].Multiple experts apply fuzzy forecasting methods to forecast the same target and collaborate by consulting each other’s forecast, subsequently modifying fuzzy forecasting method settings or forecasts [2]
An important evolving PCFI (EPCFI) diagram function is to determine the appropriate number of experts for a fuzzy collaborative forecasting task, which is critical for fuzzy group decision making [10,11,12]
The proposed evolving partial-consensus fuzzy collaborative forecasting approach is based on the EPCFI diagram
Summary
Fuzzy collaborative forecasting combines fuzzy forecasting and collaborative intelligence [1]. Most current fuzzy collaborative forecasting methods apply a fuzzy intersection (FI) to aggregate expert fuzzy forecasts [5] Though this treatment effectively elevates forecasting precision in terms of the average range of fuzzy forecasts, it has a number of drawbacks as follows. This paper proposes an evolving partial consensus fuzzy collaborative forecasting approach, where multiple experts apply various fuzzy forecasting methods to forecast the same target, and the PCFI operator is employed to aggregate the forecasts. An important EPCFI diagram function is to determine the appropriate number of experts for a fuzzy collaborative forecasting task, which is critical for fuzzy group decision making [10,11,12]. The proposed evolving partial-consensus fuzzy collaborative forecasting approach is based on the EPCFI diagram. Experts are no longer forced to modify their fuzzy forecasts when an overall consensus cannot be achieved
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