Hybrid Approach for Time Series Prediction using Neural Networks and Simulated Annealing

This paper proposes a new hybrid approach which combines simulated annealing and standard backpropagation for optimizing Multi Layer Perceptron Neural Networks (MLP) for time series prediction. Experimental tests were carried out on four simulated series with known features and on the Sunspot series. The results have shown that this approach selects the appropriate time series lags and builds an MLP with the minimum number of hidden neurons required for achieving good performance on the task. The performance attained was better than some results recently reported for hybrid systems combining Genetic Algorithms (GA) and MLPs for the same purpose