  The monitoring and management of numerous and diverse time series data at
Alibaba Group calls for an effective and scalable time series anomaly detection
service. In this paper, we propose RobustTAD, a Robust Time series Anomaly
Detection framework by integrating robust seasonal-trend decomposition and
convolutional neural network for time series data. The seasonal-trend
decomposition can effectively handle complicated patterns in time series, and
meanwhile significantly simplifies the architecture of the neural network,
which is an encoder-decoder architecture with skip connections. This
architecture can effectively capture the multi-scale information from time
series, which is very useful in anomaly detection. Due to the limited labeled
data in time series anomaly detection, we systematically investigate data
augmentation methods in both time and frequency domains. We also introduce
label-based weight and value-based weight in the loss function by utilizing the
unbalanced nature of the time series anomaly detection problem. Compared with
the widely used forecasting-based anomaly detection algorithms,
decomposition-based algorithms, traditional statistical algorithms, as well as
recent neural network based algorithms, RobustTAD performs significantly better
on public benchmark datasets. It is deployed as a public online service and
widely adopted in different business scenarios at Alibaba Group.
