Abstract: The multi-source heterogeneity and complicated relationships of spatio-temporal data of vehicle refueling bring great challenges to existing anomaly detection approaches. Aiming at the problem, a multi-view deep anomaly detection framework for vehicle refueling based on spatio-temporal data fusion was proposed. Firstly, the static information and dynamic activity data were correlated, fused and managed based on Unified Conceptual Model (UCM). Secondly, the spatio-temporal data were encoded and converted according to spatial view, temporal view and semantic view. Finally, a deep anomaly detection framework was constructed based on the above multi-views. The experimental results on vehicle refueling spatio-temporal dataset show that all anomaly detection approaches tested can achieve an average decrease in the Root Mean Square Error (RMSE) by 10.73%, and the proposed multi-view spatio-temporal anomaly detection framework can obtain a decrease in the RMSE by 19.36% compared to LSTM (Long Short-Term Memory), which gets the best results in the-state-of-the-art methods. And the Matthews Correlation Coefficient (MCC) of the proposed method on the credit card fraud dataset is increased by 32.78% compared with that of Logistic Regression model. All experimental results demonstrate the effectiveness of the proposed anomaly detection framework.

Key words: spatio-temporal data, vehicle refueling, data fusion, anomaly detection, deep learning

摘要： 车辆加油时空数据多源异构、关系复杂，现有成熟的异常检测方法难以对时空离散的加油活动数据进行分析，因此提出基于融合时空数据的车辆加油行为多视图深度异常检测框架。首先基于统一概念模型（UCM）对静态信息和动态活动数据进行关联融合管理，然后从空间视图、时间视图和语义视图角度对时空数据进行编码和转换，最后基于三种视图构建深度时空异常分析检测框架。车辆加油时空数据集上的实验结果表明，多种异常检测方法在融合时空数据上均可取得更低均方根误差（RMSE），平均降低10.73%，所提方法比现有主流方法中结果最好的长短时记忆网络（LSTM）的RMSE降低19.36%。在信用卡欺诈公开数据集上的实验结果表明，所提方法较之逻辑回归模型，马修斯系数（MCC）提高了32.78%。以上实验验证了所提方法的有效性。

关键词: 时空数据, 车辆加油, 数据融合, 异常检测, 深度学习