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There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.

Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search