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Forecasting Using First-Order Difference of Time Series and Bagging of Competitive Associative Nets
Kurogi, S.; Koyama, R.; Tanaka, S.; Sanuki, T.;
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
12-17 Aug. 2007
Page(s):166
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171
Abstract:
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.
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