  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.
