CAT SWARM OPTIMIZATION BASED CLUSTERING ALGORITHM FOR FUZZY TIME SERIES FORECASTING
This paper presents the development of an improved Fuzzy Time Series (FTS) forecasting model with Cat Swarm Optimization based Clustering (CSO-C) algorithm. FTS forecasting is affected by some of the subjective decisions made at the fuzzification stage like size of interval length or universe of discourse, and approaches such as clustering, trend-mapping, have been adopted to address this. Traditional clustering techniques, such as fuzzy C-means (FCM) and K-means used to tackle the problems associated with the fuzzification stage encountered problems such as handling high dimensional data, sensitivity to noise and outliers and pre-mature convergence. In this paper, CSO-C algorithm was developed using MATLAB 2015a to address pre-mature convergence with a view to improving forecast accuracy in FTS forecasting. The developed model was tested on student enrolment of University of Alabama and Taiwan Future Exchange (TAIFEX). Also applied to forecast the student enrolment of University of Maiduguri (UniMaid) data. In all cases, Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used as the performance metrics. The developed model presents itself superior, with reduced prediction error when compared with previously existing models in the literature.