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

@article{Kurogi2007ForecastingUF,
  title={Forecasting Using First-Order Difference of Time Series and Bagging of Competitive Associative Nets},
  author={Shuichi Kurogi and Ryohei Koyama and Shinya Tanaka and Toshihisa Sanuki},
  journal={2007 International Joint Conference on Neural Networks},
  year={2007},
  pages={166-171}
}
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… CONTINUE READING