论文标题
GenAD：多元时间序列的一般表示，用于异常检测
GenAD: General Representations of Multivariate Time Seriesfor Anomaly Detection
论文作者
论文摘要
中国移动中无线基站的可靠性至关重要，因为手机用户已连接到电台，并且电台的行为与用户体验直接相关。尽管可以通过多变量时间序列上的异常检测来实现对站行为的监视，但由于复杂的相关性和大规模站中多元序列的各种时间模式，建立了一个普遍的无处可比性的异常检测模型，其F1分数较高仍然是一项挑战的任务。在本文中，我们提出了用于异常检测的多元时间序列的一般表示（GenAD）。首先，我们在具有自upervision的大型无线基站上预先培训了一个通用模型，可以轻松地将其转移到具有少量训练数据的特定站异常检测到特定站的异常检测中。其次，我们采用多相关的关注和时间序列的关注来代表电台的相关性和时间模式。通过上述创新，Genad在中国移动中的实际数据集中将F1得分总数提高了9％，而在公共数据集中，只有10％的培训数据的绩效并没有大大降级。
The reliability of wireless base stations in China Mobile is of vital importance, because the cell phone users are connected to the stations and the behaviors of the stations are directly related to user experience. Although the monitoring of the station behaviors can be realized by anomaly detection on multivariate time series, due to complex correlations and various temporal patterns of multivariate series in large-scale stations, building a general unsupervised anomaly detection model with a higher F1-score remains a challenging task. In this paper, we propose a General representation of multivariate time series for Anomaly Detection(GenAD). First, we pre-train a general model on large-scale wireless base stations with self-supervision, which can be easily transferred to a specific station anomaly detection with a small amount of training data. Second, we employ Multi-Correlation Attention and Time-Series Attention to represent the correlations and temporal patterns of the stations. With the above innovations, GenAD increases F1-score by total 9% on real-world datasets in China Mobile, while the performance does not significantly degrade on public datasets with only 10% of the training data.
