An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

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.

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

“…When multiple decision makers are involved, the decision-making problem becomes a group-based one. However, most past studies assumed that there was an overall consensus among all decision makers, and just averaged decision makers' judgements before applying a fuzzy multiple-criteria decision-making method, which was problematic because sometimes it was difficult for decision makers to reach an overall consensus [15][16][17]27,31]. In addition, the averaging result may be meaningless to decision makers [44].…”

“…In addition, the averaging result may be meaningless to decision makers [44]. To address this issue, the partial consensus among some decision makers can be sought instead [15][16][17]. This study also belongs to this type of research.…”

“…(1) The proposed methodology does not assume the existence of an overall consensus among all decision makers, which is more practical. (2) The partial consensus among some decision makers may not be obvious using existing methods, such as Wang and Chen [15], Lin and Chen [16], and Chen et al [17]. In contrast, the proposed methodology varies the number of decision makers that reach a partial consensus to ensure that the partial consensus is obvious enough.…”

“…• Fuzzy arithmetic averages are applied to aggregate decision makers' judgements, which may lead to unreasonable results [13,14]. • Decision makers may not reach a consensus about the priorities of factors critical to an intervention strategy [15][16][17].…”

“…When the overall consensus among all decision makers is lacking, the partial consensus among most decision makers [15][16][17]27] is sought instead.…”