基于多模态生成对抗网络的多元时序数据异常检测

Multimodal Generative Adversarial Networks Based Multivariate Time Series Anomaly Detection

针对传统多元时序数据异常检测模型未考虑时空数据的多模态分布问题,提出了一种多模态生成对抗网络多元时序数据异常检测模型。利用滑动窗口分割时间序列并构造特征矩阵来捕获数据的多模态特征,将其与原始数据分别作为模态信息输入多模态编码器及多模态生成器中,输出具有时空信息的多模态特征矩阵,并将真实数据编码成特征矩阵,将两类特征矩阵作为判别器输入,利用梯度惩罚方法并拟合真实分布与生成分布之间的Wasserstein距离,取代二分类交叉熵损失训练判别器,结合生成器重构误差及判别器评分实现异常检测。基于安全水处理(SWaT)及水量分布(WADI)等数据集的测试结果表明,所提模型相比基准模型在F1-分数性能指标上分别提升了0.11和0.19,能够较好地识别多元时序数据异常,具有较好的鲁棒性以及泛化能力。

Aiming at the problem that the traditional anomaly detection model of multivariate time series data does not consider the multimodal distribution of spatio-temporal data,a multivariate time series data anomaly detection model based on multimodal generative adversarial networks is proposed.The sliding windows is used to segment the time series and construct feature matrices,so as to capture the multimodal features of the data.Feature matrix and raw data are fed into the multimodal encoder and multimodal generator as modal information respectively,then multimodal feature matrix with spatio-temporal information is outputted.The real data is encoded into feature matrices and the two types of feature matrices are utilized as discriminator inputs.In the proposed method,a gradient penalty method and the Wasserstein distance between the real and generated distributions to replace the binary cross-entropy loss are utilized to train the discriminator,then combining the generator reconstruction error and discriminator scores to detect anomalies.Experimental results based on the secure water treatment(SWaT)and the water distribution(WADI)datasets show that,compared with the baseline model,the proposed method improves the F1-score metrics by 0.11 and 0.19 respectively.The proposed method can identify multivariate time series data anomalies well,with good robustness and generalizability.