A model for sales forecasting based on fuzzy clustering and Back-propagation Neural Networks with adaptive learning rate

This paper attempts to describe hybrid sales forecasting system based on fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learning rate (FCBPN). The proposed approach is composed of three stages: (1) Winter's Exponential Smoothing method will be utilized to take the trend effect into consideration;(2) utilizing Fuzzy C-Means clustering method, the clusters membership levels of each normalized data records will be extracted; (3) Each cluster will be fed into parallel BP networks with a learning rate adapted as the level of cluster membership of training data records. Compared to many researches which use Hard clustering, we employ fuzzy clustering which permits each data record to belong to each cluster to a certain degree, which allows the clusters to be larger which consequently increases the accuracy of the proposed forecasting system. Therefore, it is a very promising solution for industrial forecasting.