基于knnVAR模型的地理传感数据预测

Prediction of Geosensor Data Based on knnVAR Model

地理传感数据的预测在经济、工程、自然科学和社会科学中被广泛应用。数据中不同站点的空间相关性和同一站点的时间相关性给传统的预测方法带来了极大的挑战。文中提出了一种将数据中时间信息和空间信息有效融合,同时考虑了各传感序列独特性的knnVAR模型,来对地理传感数据进行预测。该模型通过计算时空距离量化数据中的时间信息和空间信息,并基于时空距离寻找K近邻,最后再将近邻结果应用于向量自回归模型中完成预测。knn-VAR模型采用寻找时空近邻的方式将数据中时间维度和空间维度的相关性进行有效融合,同时使用在时空上具有高度相关性的近邻对传感序列进行预测,充分考虑了各地理序列的独特性。实验结果表明,knnVAR模型能有效提高地理传感数据的预测精度。

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.