Price Forecasting for Cell Phone Market Using Adaptive Sliding Window and Adaptive RBF NN - Quan-Yin Zhu; Su-Qun Cao; Pei Zhou; Yonghua Yin (Journal of Algorithms & Computational Technology, 2013)
In order to get the excellent accuracy for price forecast in the cell phone market, a novel improved Sliding Window (SW) model based on adaptive windows width and a novel improved Radial Basis Function (RBF) Neural Network (NN) model based on adaptive spread are proposed and the Disturbance Factors Model (DFM) is used in this paper. All of the three kinds of price forecasting models are utilized to verify the accuracy. The cell phone price is extracted from different websites and used as the model verification data. And the experimental results of the forecasting average accuracy based on the DFM obtain 94.61 percent. The experimental results of the forecasting average accuracy based on the ARBF NN model obtain 97.88 percent. The experimental results of the forecasting average accuracy based on the Adaptive SW Model (ASWM) obtain 99.64 percent. Although the results based on the DFM are not very good, it is still a satisfactory result. Since it is at least not a very serious result which proves that it is worth to do further researches in the field of the cell phone market based on the DFM. The results based on the ASWM and the ARBF NN models are satisfied. The improved methods enhance the forecast accuracy compared to the original model. In the field of the price forecast on the cell phone market, the improved methods have a good performance which is valuable and useful not only for businesses, but also for consumers.