An architechture for intelligent assistance in the forecasting process

The need to effectively integrate decision-making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge-based techniques. In the present paper we explore the potential benefits of such an integration in the area of statistical forecasting. In an attempt to clarify the elements of the forecasting process which could be enhanced by intelligent assistance, a generic forecasting process is described and its main functional elements are identified. lIthe paper describes the architecture of such a system (Intelligent Forecasting Information System, ZFZS), in which, besides the traditional components of a decision-support system four constituents are included that try to model the expertise required: a Z+ocess Expert, a Learning Expert, a Data Expert and a Model Expert.