顾及缺失值的因果图时空预测网络

A causal graph convolutional network considering missing values for spatio-temporal prediction

时空预测是地理时空大数据挖掘的基础研究命题。目前,多种模型用于预测未知系统的时空状态。然而,存在的大多数预测模型仅在没有缺失数据的时空数据集上进行测试,忽略了缺失值对预测结果的影响。在真实场景中,由于传感器或网络传输故障,数据缺失是一个不容忽视的问题。鉴于此,本文提出了一种顾及缺失值的因果图卷积网络(causal graph convolutional network considering missing values,Causal-GCNM)模型用于时空预测。Causal-GCNM模型可以自动捕捉时空数据中的缺失模式,使得Causal-GCNM模型在不需要借助额外插值算法的前提下,可以直接完成时空预测任务。本文提出的模型在3种真实的时空数据集(交通流数据集、PM_(2.5)监测数据集及气温监测数据集)得到了验证。试验结果表明,Causal-GCNM模型在4种缺失条件(20%随机缺失、20%块状缺失、40%随机缺失及40%块状缺失)下仍然具有较好的预测性能,并在预测精度和计算效率两类指标上优于10种存在的基线方法。

Spatio-temporal prediction is one of the basic research topics of geographic spatio-temporal big data mining.There are many attempts to predict spatio-temporal state of unknown systems using various deep learning algorithms.However,most existing prediction models are only tested on spatio-temporal data assuming no missing data entries,ignoring the impact of missing values on the prediction results.In the actual scenarios,data missing is an inevitable problem due to sensor or network transmission failures.Therefore,we propose a novel causal graph convolutional network considering missing values(Causal-GCNM)for spatio-temporal prediction.The proposed model can automatically capture missing patterns in the spatio-temporal data,enabling the Causal-GCNM model to directly complete the spatio-temporal prediction task without additional interpolation.The proposed model was validated on three real spatio-temporal datasets(traffic flow dataset,PM_(2.5) monitoring dataset,and temperature monitoring dataset).Experimental results show that the Causal-GCNM model has good prediction performance under four missing scenarios(20%random missing,20%block missing,40%random missing,40%block missing),and outperforms ten existing baseline methods in terms of prediction accuracy and computational efficiency.