Abstract: The prediction of geosensor data is widely used in economy,engineering,natural science and social sciences.The spatial correlation of different sites and the time correlation of the same site in the data pose great challenges to traditional forecasting models.In this paper,a knnVAR model which computes the relevance of the space-time information effectively and considers the uniqueness of each sensing sequence at the same time was proposed to predict the geosensor data.This model quantifies the time information and spatial information of the data by calculating the space-time distance,and then searches for the K nearest neighbor based on space-time distance.Finally,the nearest neighbor sequences were applied to the vector autoregressive model.By searching for space-time nearest neighbors,knnVAR model computes the relevance of the time dimension and space dimension effectively.At the same time,knnVAR model uses the space-time nearest neighbor sequences which are highly correlated to predict the sensing sequence.The experimental results show that the knnVAR model can improve the prediction accuracy of geosensor data effectively.

Key words: Geosensor data, K nearest neighbor, Space-time distance, Vector autoregressive model