Deep Belief Network for Meteorological Time Series Prediction in the Internet of Things

Abstract

Seeking to address the challenges associated with high-dimensional complex time series representations of recurrent neural networks, such as low generalization ability and long training time, a hybrid neural network based on a deep belief network (DBN) is proposed in this paper to facilitate time series predictions for the Internet of Things. In our approach, we integrate both a DBN and a recurrent neural network with the gated recurrent unit as the activation unit. First, we implement unsupervised pretraining through the DBN and then supervise the curve fitting using the recurrent neural network. Finally, the hybrid neural network is learned and can make predictions. The experimental results show that the hybrid neural network has a stronger historical learning ability than two other widely used recurrent neural networks and can effectively reduce the number of iterations required by the recurrent neural network, thereby reducing the overall learning time.