THE LINGUISTIC FORECASTING OF TIME SERIES USING IMPROVED FUZZY COGNITIVE MAP

Most researchers of time series forecasting devote to design and develop quantitative models for pursuing high accuracy of forecasting on the numerical level. However, in real world, the numerical accuracy is sometimes not necessary for human cognition and decision-making and the numerical results of forecasting based on quantitative model are deficient in interpretability, thus the development of qualitative forecasting model of time series becomes an evident challenge. In this paper, the improved fuzzy cognitive map (IFCM) are proposed first, and then it is applied to develop qualitative model for linguistic forecasting of time series together with fuzzy c-means clustering technology and real-coded genetic algorithm (RCGA). Two real life time series are used to test the developed forecasting model and compare with another method based on FCM, whose results show the developed FCM forecasting model is more simpler and high quality on the linguistic level.