A New Method of Modeling Spatio-temporal Sequence by Considering Spatial Scale Characteristics

The paper presents a new method of modeling spatio-temporal sequence data by considering spatial scale characteristics, where original data is regarded as the combination of a general trend term representing at a larger scale and a local bias term representing at a smaller scale. The original data is first converted into a larger-scale data that represents the general trend of original data. Then, this general trend term is removed and the left part is the bias term of original data. Finally, prediction models of the trend term and the bias term are respectively built, and the combination of the two models is used to predict the original spatio-temporal sequence. Application in annual precipitation and daily average of PM2.5 concentration shows that this proposed method can be used to obtain multi-scale spatial predictions, and is better in accuracy than those models without consideration of spatial scale characteristics.