Abstract: It is difficult to effectively forecast overall sales volume of short life cycle experiential products using traditional methods with data point forecasts during the preparation period, because of fuzzification of variables and a lack of historical sales data. To address this problem, a method for forecasting interval reliability classification of short life cycle experiential product during preparation period is proposed based on rough set and evidence theory. This method use rough set theory to get reliability function from each variables and use evidence theory to set up comprehensive reliability. The comprehensive reliability value of the training set is used to construct the classification reliability interval, so as to judge the classification reliability interval from the comprehensive reliability value of the test set , and obtain the classification reliability interval of the test set finally. 621 samples selected from domestic films released during 2016-2018 are used to verify the effectiveness of the proposed method, and the intersectional verification results shows the forecasting method has great accuracy.

Key words: Demand forecasting, short life cycle experiential product, d-s evidence theory ;rough set, box office

摘要： 缺乏历史销售数据且影响变量模糊难以保证筹备期短生命周期体验品点需求预测方法的有效性。针对该问题，提出了一种结合粗糙集与证据理论的筹备期短生命周期体验品区间信度需求预测方法。该方法使用粗糙集获取样本各变量信度分配函数值；使用证据理论将各变量信度分配函数值进行合成，得到各样本的综合信度值；使用训练集样本综合信度值构建分类区间，判断测试集样本综合信度值归属区间，得到测试集分类信度区间结果。选取2016~2018年621部国产电影验证了方法的有效性；交叉验证结果显示方法具有良好的准确性。

关键词: 需求预测, 短生命周期体验品, 证据理论, 粗糙集, 电影票房