To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the recurrent neural network for the time series prediction. The experimental results show that our approach gives good performance in predicting the missing values from the time series.
N. Zhang et al., "Time Series Prediction with Recurrent Neural Networks using a Hybrid PSO-EA Algorithm," Proceedings of the IEEE International Joint Conference on Neural Networks, 2004, Institute of Electrical and Electronics Engineers (IEEE), Jan 2004.
The definitive version is available at https://doi.org/10.1109/IJCNN.2004.1380208
IEEE International Joint Conference on Neural Networks, 2004
Electrical and Computer Engineering
Keywords and Phrases
IJCNN 2004; Evolutionary Computation; Evolutionary Learning Algorithm; Global Optimization Methods; Hybrid Algorithm; Learning (Artificial Intelligence); Optimisation; Particle Swarm Optimization; Recurrent Neural Nets; Recurrent Neural Networks; Time Series; Time Series Prediction
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.