A Rule-based Forecasting System Integrating combining and Single Forecast for Decision Making

Abstract

Many studies demonstrated that combining forecasts produces consistent but modest gains in accuracy. However, little researches define well the conditions under neither which combining is most effective nor how methods should be combined in each situation. In this paper, a rule-based forecasting system industry is proposed in order to compare forecast performance between combining forecasts and single forecasts, then define these conditions and to specify more effective combinations, finally suggest the best methods. Two comparative case studies for the telecommunications and TFT-LCD industry are proposed to examine the performance of the proposed system. Results from this study indicate that combining forecasts outperform single forecasts only when data set is data have various nonlinear characteristics. In this research, empirical evidence shows that rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals.