A divide-and-conquer method for space-time series prediction

作者：Deng, Min; Yang, Wentao^*; Liu, Qiliang; Zhang, Yunfei

来源：Journal of Geographical Systems, 2017, 19(1): 1-19.

DOI：10.1007/s10109-016-0241-y

• Space-time series prediction
• Spatial scale
• Scale characteristics
• Space-time series clustering

Space-time series can be partitioned into space-time smooth and space-time rough, which represent different scale characteristics. However, most existing methods for space-time series prediction directly address space-time series as a whole and do not consider the interaction between space-time smooth and space-time rough in the process of prediction. This will possibly affect the accuracy of space-time series prediction, because the interaction between these two components (i.e., space-time smooth and space-time rough) may cause one of them as dominant component, thus weakening the behavior of the other. Therefore, a divide-and-conquer method for space-time prediction is proposed in this paper. First, the observational fine-grained data are decomposed into two components: coarse-grained data and the residual terms of fine-grained data. These two components are then modeled, respectively. Finally, the predicted values of the fine-grained data are obtained by integrating the predicted values of the coarse-grained data with the residual terms. The experimental results of two groups of different space-time series demonstrated the effectiveness of the divide-and-conquer method.

• 出版日期2017-1
• 单位中南大学