Title :
Daily peak temperature forecasting with Elman neural networks
Author :
Vitabile, S. ; Pernice, M. ; Gaglio, S.
Author_Institution :
Italian Nat. Res. Council, ICAR, Palermo, Italy
Abstract :
This work presents a forecaster based on an Elman artificial neural network trained with resilient backpropagation algorithm for predicting the daily peak temperatures one day ahead. The available time series was recorded at Petrosino (TP), in the west coast of Sicily, Italy and it is composed by temperature (min and max values), the humidity (min and max values) and the rainfall value between January 1st, 1995 and May 14th, 2003. Performances and reliabilities of the proposed model were evaluated by a number of measures, comparing different neural models. Experimental results show very good prediction performances.
Keywords :
backpropagation; neural nets; time series; weather forecasting; Elman neural network; daily peak temperature forecasting; resilient backpropagation algorithm; time series; Artificial neural networks; Backpropagation algorithms; Computer networks; Electronic mail; Load forecasting; Neural networks; Performance evaluation; Predictive models; Temperature; Weather forecasting;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1381091
