Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data
Hanchi Ren, Yeqing Wang, Huijun Ma
Abstract
To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively improve the performance of prediction. Moreover, the variance is obtained to evaluate the prediction results. The model is validated on the real weather dataset in Beijing. The experiments show that LSTM and GRU have their pros and cons for different data, CI fusion can improve the accuracy of the final predictions, and the entire framework has robust prediction results with a reasonable estimated variance.