Nonlinear integration of spatial and temporal forecasting by support vector machines

Spatio-temporal data mining is the extraction of unknown and implicit knowledge, structures, spatio-temporal relationships, or patterns not explicitly stored in spatio-temporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatio-temporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension respectively, or they are combined linearly as spatio-temporal integration. However, such linear combination of spatial and temporal dimensions is just a simplification of complicated spatio-temporal associations existing in complex geographical phenomena. In this study, The Support Vector Machines is introduced to construct nonlinear combination functions related to the spatial and temporal dimensions for integrated spatio-temporal forecasting. The proposed method has been tested by forecasting the annual average temperature of meteorological stations in P R China. The forecasting result shows that nonlinearly integrated spatio-temporal forecasting model via Support Vector Machines obtained better forecasting accuracy than those obtained by linear combination and other conventional methods.