Neural network ensembles for time series forecasting

This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous studies have tested the EPNet algorithm in the classification field, taking the best individuals to solve the problem and creating ensembles to improve the performance. But no studies have analyzed the behavior of the algorithm in detail for time series forecasting, nor used ensembles to try to improve the predictions. Thus, the aim of this work is to compare the ensemble approach, using two linear combination methods to calculate the output, against the best individual found. Since there are several parameters to adjust, experiments are set up to optimize them and improve the performance of the algorithm. The algorithm is tested on 21 time series of different behaviors. The experimental results show that, for time series forecasting, it is possible to improve the performance by using the ensemble method ...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Victor M. Landassuri-Moreno, John A. Bullinaria
• Keywords: Algorithm, Artificial Intelligence, GECCO 2009, Neural Networks, Time Series Forecasting