Abstract
General statistical method such as the Box-Jenkins ARIMA(p,d,q) model have long been applied in forecasting. Statistical methods such as auto-regression has been used as an efficient and accurate way for forecasting in certain applications such as stock-market forecasting. However, one still has to monitor the forecasting system and determine whether to adjust the parameters to reduce forecasting errors when applying auto-regressive method. A recurrent neural network has been designed to make the forecasts of auto-regression. Then the weight adjusting strategies of the recurrent neural network can be used to continuously adjust the parameters based on the forecasting errors. Therefore, we obtain the forecasts efficiently based on auto-regression without having to monitor the forecasting system constantly and adjust the parameters manually. This provides a very effective tool in forecasting monthly cyclic trends in importing and exporting in a harbor.
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© 1998 Springer-Verlag
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Wu, Si. (1998). Cyclic forecasting with recurrent neural network. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_819
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DOI: https://doi.org/10.1007/3-540-64582-9_819
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