Forecasting retail sales based on cheng fuzzy time series and particle swarm optimization clustering algorithm

Abstract

Use of the conventional forecasting method, which is based on trend data with average sales in the last few months, results inaccurate forecasting due to a large difference in data, this is the same as fuzzy forecasting with the same interval length or static. Therefore, this paper recommends using the Cheng forecasting method combined with the Particle Swarm Optimization (PSO) algorithm. We use an artificial intelligence, i.e., PSO algorithm to set non-static length of intervals each cluster on Cheng method. The comparison of this method yields a better root mean square error (RMSE) value for each cluster on the recommended method.