An Integrated Spatio-Temporal Forecasting Approach Based on Data Fusion and Method Fusion

Spatio-temporal data mining is an important research topic in data mining, and in which spatio-temporal forecasting is the most widely used. In order to overcome the limitations of current spatio-temporal forecasting methods, this paper proposes a spatio-temporal forecasting approach based on data fusion and method fusion. The approach first forecasts time sequence of the target object itself with statistical principles and computes influences of neighboring objects employing neural network technique, then forecasts mixed data sequence using spatio-temporal auto-regressive model, and finally integrates the individual time sequence forecast, spatial forecast and spatio-temporal forecast through linear regression to deliver the final result. The approach was successfully used in the forecasting of railway passenger flow in an attempt to overcome the limitations of traditional railway passenger flow forecasting methods. The experimental result shows the effectiveness of the approach.