关键词: 离群点检测, 时间序列, 高斯过程, 异方差, 变分推断

Abstract: Generally, there are inevitable disturbances in time series data, such as inherent uncertainties and external interferences. To detect outlier in time series data with time-varying disturbances, an approach based on prediction model using Gaussian Processes was proposed. The monitoring data was decomposed into two components:the standard value and the deviation term. As the basis of model for the ideal standard value without any deviation, Gaussian processes were also employed to model the heteroscedastic deviations. The posterior distribution of predicted data which is analytically intractable after introducing deviation term was approximated by variational inference. The tolerance interval selected from posterior distribution was used for outlier detection. Verification experiments were conducted on the public time series datasets of network traffic from Yahoo. The calculated tolerance interval coincided with the actual range of reasonable deviation existing in labeled normal data at various time points. In the comparison experiments, the proposed model outperformed autoregressive integrated moving average model, one-class support vector machine and Density-Based Spatial Clustering of Application with Noise (DBSCAN) in terms of F1-score. The experimental results show that the proposed model can effectively describe the distribution of normal data at various time points, achieve a tradeoff between false alarm rate and recall, and avoid the performance problems caused by improper parameter settings.

Key words: outlier detection, time series, Gaussian process, heteroscedasticity, variational inference