面向空气质量预测的多粒度突变拟合网络

Multi-granularity abrupt change fitting network for air quality prediction

空气质量数据作为一种典型的时空数据,具有复杂的多尺度内在特性并存在突变的问题。针对现有空气质量预测方法在处理包含大量突变数据的空气质量预测任务时表现不佳的问题,提出一种面向空气质量预测的多粒度突变拟合网络(MACFN)。首先,针对空气质量数据在时间上的周期性,对输入数据进行了多粒度的特征提取。然后,采用图卷积网络与时间卷积网络分别提取空气质量数据的空间关联性与时间依赖性。最后,设计一个突变拟合网络自适应地学习数据中的突变部分,从而减小预测误差。所提网络在3个真实的空气质量数据集上进行了实验评估,与多尺度时空网络(MSSTN)相比,均方根误差(RMSE)分别下降约11.6%、6.3%和2.2%。实验结果表明,MACFN能有效捕捉复杂的时空关系,并在变化幅度较大、易发生突变的空气质量预测任务中有更好表现。

Air quality data,as a typical spatio-temporal data,exhibits complex multi-scale intrinsic characteristics and has abrupt change problem.Concerning the problem that existing air quality prediction methods perform poorly when dealing with air quality prediction tasks containing large amount of abrupt change,a Multi-Granularity abrupt Change Fitting Network(MACFN)for air quality prediction was proposed.Firstly,multi-granularity feature extraction was first performed on the input data according to the periodicity of air quality data in time.Then,a graph convolution network and a temporal convolution network were used to extract the spatial correlation and temporal dependence of the air quality data,respectively.Finally,to reduce the prediction error,an abrupt change fitting network was designed to adaptively learn the abrupt change part of the data.The proposed network was experimentally evaluated on three real air quality datasets,and the Root Mean Square Error(RMSE)decreased by about 11.6%,6.3%,and 2.2%respectively,when compared to the Multi-Scale Spatial Temporal Network(MSSTN).The experimental results show that MACFN can efficiently capture complex spatio-temporal relationships and performs better in the task of predicting air quality that is prone to abrupt change with a large magnitude of variability.