Forecasting Using First-Order Difference of Time Series and Bagging of Competitive Associative Nets
Shuichi Kurogi, Ryohei Koyama, Shinya Tanaka, Toshihisa Sanuki · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
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.