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Keywords
Management, Forecasting, Information systems, Modelling
Abstract
The need effectively to 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. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first-named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision support onformation system, four constituents are included that try tc model the expertise required. The information system adopts an object-oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3-competition monthly data.
1. Introduction
The need to effectively integrate decision making together with the knowledge representation tasks and inference procedures that model an expert's thought process has provoked recent research efforts to integrate decision support systems (DSS) with knowledge-based expert systems (Steinke and Nickolette, 2003; Wang, 2001; Eom, 1999; Nord and Nord, 1997). Various forms of this integration have been examined and a variety of systems architectures have been proposed (see for example Metaxiotis et al., 2002, Prasad, 2000; Balachandra, 2000; Walker, 2000; Beckett et al., 2000; Portougal and Janczewski, 1998; El-Najdawi and Stylianou, 1993; Armstrong and Collpoy, 1993; Assimakopoulos et al., 1993; Fildes and Beard, 1992; Gottinger and Weinmann, 1992; Edwards, 1992; King, 1990; Turban, 1990; Lin and Hatcher, 1989; Rockart and De Long, 1988; Turban and Watkins, 1986). Moreover, based on broad classification of current types of information systems, Mentzas (1994a) identified some of the essential features for intelligent decision-making support. Among them, issues related to interpretation, reasoning, and learning seem to be the most crucial for enhancing the mission-critical elements of corporate decision making; see e.g. Mentzas (1994b) for an illustration of these features in the production management environment.
Business forecasting seems to be an area in which such an integration of decision support with intelligent features could...