Gaussian process modeling and multi-step prediction for time series data in wireless sensor network environmental monitoring - Yan CHEN; Zi-jian WANG; Ze ZHAO; Dong LI; Li CUI (Tongxin xuebao)
For time series data collected from WSN environmental monitoring applications,a novel multi-step prediction method based on Gaussian process model was proposed.The method could make prediction for future environmental monitoring data.Kernel functions were used to describe data properties in the Gaussian process model.Kernel functions for environmental monitoring data were constructed through the EMD(empirical mode decomposition)technique and analysis of data inherent physical properties.And the constructed kernel functions were capable of describing the data change mode.Extensive experiments for multi-step prediction performance comparison test were performed on three kinds of data sets using over 20 000 environmental monitoring data records.Experimental results show that the average prediction accuracy of the Gaussian process multi-step prediction method can be increased by 20% than compared prediction methods.The prediction method can be applied to future environmental parameters trend analysis,early warning for abnormal environmental events and other scenes.