Forecasting Using First-Order Difference of Time Series and Bagging of Competitive Associative Nets

This article describes our method used for the 2007 forecasting competition for neural networks and computational intelligence. We have employed the first-order difference of time series for dealing with the seasonality of the monthly data. Since the differencing removes the trend of time series, we have developed a method to estimate the trend. Moreover, we have used the bagging of competitive associative net called CAN2 as a learning predictor, where the CAN2 is for learning an efficient piecewise linear approximation of a nonlinear function, and the bagging for reducing the variance of the prediction.