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.
翻译:中国移动型无线基站的可靠性至关重要,因为手机用户与台站相联,台站的行为与用户经验直接相关。虽然通过多变时间序列的异常探测可以实现对台站行为的监测,但由于在大型台站的复杂相关关系和多变系列的各种时间模式,在大型台站建立一般不受监督的异常探测模型,高F1分数仍是一项具有挑战性的任务。在本文中,我们提议为异常探测(GenAD)提供一个多变时间序列的一般代表。首先,我们预先设计了一个带有自我监督功能的大型无线基站的一般模型,该模型可以很容易地传输到特定台站的异常探测中,并有少量的培训数据。第二,我们采用多调控和时序关注来代表台站的关联和时间模式。由于上述创新,GenAD在中国移动型实体世界数据集中将F1分数总共增加9%,而业绩并没有显著降低公共数据集的功能,只有10%的培训数据。