Paper Info
Title
Cyclic Forecasting with Recurrent Neural Network
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.
Year
DOI
Venue
1998
10.1007/3-540-64582-9_819
IEA/AIE (Vol. 1)
Keywords
Field
DocType
cyclic forecasting,recurrent neural network,auto regressive
Technology forecasting,Data mining,Autoregressive model,Recurrent neural nets,Computer science,Recurrent neural network,Autoregressive integrated moving average,Artificial intelligence,Probabilistic forecasting,Machine learning
Conference
ISBN
Citations 
PageRank 
3-540-64582-9
0
0.34
References 
Authors
2
1
Name
Order
Citations
PageRank
Shaun-Inn Wu1182.95