移动互联网时代，人们既是数据的生产者又是数据的消费者，从而导致交互信息大量产生。组合预测能有效融合多源信息和分散预测风险，受到学术界广泛重视。传统组合预测隐含信息的加性或乘性可分离的独立性假设，无法体现交互信息的融合特征。本项目在此背景下，探讨基于交互信息集成的组合预测方法及其应用。本项目拟定义交互信息环境下新的群组一致性测度，试图给出基于统计和关系代数的冗余单项预测识别方法，探讨固定规模约束下最优组合预测模型的筛选技术。同时拟结合网络模型，提出信息交互水平的测度和多种广义交互信息融合函数等概念，研究其数理性质，并构建相应的最优组合预测模型。针对该模型，探讨交互信息的优性或非劣性组合预测存在的充要条件和智能求解算法。在预测全过程视角下，拟通过DEA交叉效率评价模型获取组合预测应用框架，并对交互信息典型的旅游产业进行实证分析。本项目是大数据时代具有重要理论创新意义和实际应用价值的研究课题。

In the mobile internet era, people are both producers and consumers of data, which results in a large amount of mutual information. Combination forecasting can fuse multi-source information effectively and disperse forecasting risk, which has given rise to many attentions of scholars. Traditional combined forecasting implies the assumption that the information has the independence with additive or multiplicative separation. Therefore, it can't reflect interactive characteristics of information fusion. The project discusses the combination forecast method based on mutual information integration and applications under this background. This project intends to define new group consistency measure under the interactive information environment, try to give redundant single forecasting recognition methods based on statistics and relational algebra, and discusses the screening technology of the optimal combination forecasting model with the constraints of fixed forecasting size. At the same time, we are going to propose the concepts of the measure of information interactive level by the network model, and a variety of generalized interactive information fusion functions, and we will also study their mathematical properties, and construct the corresponding optimal combination forecast models. This project will discuss the necessary and sufficient conditions of the existence of superiority or non-inferiority combined forecasting with the mutual information and intelligent algorithms for these new models. In the whole process perspective of prediction, we will obtain the application framework by DEA cross efficiency evaluation model for combination forecasting, and provide the demonstration analysis on the tourism industry with the typical information interaction. This project is a research topic with important theoretical innovation meanings and practical application values in a big data era.