New technology product demand forecasting using a fuzzy inference system

Abstract

This work presents a fuzzy inference system for forecasting the product demand of a new technology. Recent studies have addressed the problems of new technology product demand forecasting using different methods including artificial neural networks and model-based approaches. In this study, we propose to use a hybrid intelligent system called adaptive neuro-fuzzy inference system (ANFIS) for forecasting computer demand. In ANFIS, both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic are combined in order to provide enhanced forecasting capabilities, compared to using a single methodology alone. After training ANFIS and checking for forecasting, it was found that the root-mean-square error and other common error measures can be reduced in comparison with two other conventional models (autoregressive and autoregressive moving average).