Abstract
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.
Highlights
Time series prediction is an important area of prediction in which past values of the same variable are collected and analyzed to develop a model describing the underlying relationship
We propose and evaluate the use of Harmony Search, a meta-heuristic, in parameter selection of Deep Belief Networks for time series prediction
Experimental results obtained reveal that the Deep Belief Networks (DBNs) method with the main parameters selected by Harmony Search performs better than DBN model with the main parameters selected by the random method or by Particle Swarm Optimization (PSO) method in most of the tested datasets
Summary
Time series prediction is an important area of prediction in which past values of the same variable are collected and analyzed to develop a model describing the underlying relationship. There exist some well-known methods of time series prediction, such as ARIMA, exponential smoothing, artificial neural networks (ANNs), k-nearest-neighbors algorithm and support vector machines (SVMs). We propose a method of parameter selection for DBNs in time series prediction which is based on Harmony Search algorithm ([19], [20]). Ii) We compare the performance of Harmony Search method to that of Particle Swarm Optimization (PSO) in parameter selection for DBNs in time series prediction.
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