一种基于数据融合和方法融合的时空综合预测算法

时空数据挖掘是数据挖掘中的重要研究内容，其中时空预测的应用领域最为广泛．针对目前时空预测方法中的不足，提出了一种基于数据融合和方法融合的时空综合预测算法．该方法首先采用统计学原理对目标对象本身的时序进行预测；然后通过神经网络解算相邻对象的空间影响，继而对混合数据序列使用时空自回归预测模型；最后使用线性回归将单个的时间预测、空间预测和时空预测有效地融合在一起，得到综合预测结果．应用该方法预测铁路客流，突破了传统铁路客流预测方法的局限，实验结果表明了算法的有效性．

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.