A NOVEL APPROACH BASED ON GENETIC FUZZY CLUSTERING AND ADAPTIVE NEURAL NETWORKS FOR SALES FORECASTING

Abstract

This article proposes a new hybrid sales forecasting system based on genetic fuzzy clustering and Back-Propagation (BP) Neural Networks with adaptive learning rate (GFCBPN).The proposed architecture consists of three stages: (1) utilizing Winter’s Exponential Smoothing method and Fuzzy C-Means clustering, all normalized data records will be categorized into k clusters; (2) using an adapted Genetic Fuzzy System (MCGFS), the fuzzy rules of membership levels to each cluster 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 previous researches which use Hard clustering, this research uses the fuzzy clustering which capable to increase the number of elements of each cluster and consequently improve the accuracy of the proposed forecasting system. Printed Circuit Board (PCB) will be utilized as a case study to evaluate the precision of our proposed system. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising method for financial forecasting.