  Deep learning has seen increasing applications in time series in recent
years. For time series anomaly detection scenarios, such as in finance,
Internet of Things, data center operations, etc., time series usually show very
flexible baselines depending on various external factors. Anomalies unveil
themselves by lying far away from the baseline. However, the detection is not
always easy due to some challenges including baseline shifting, lacking of
labels, noise interference, real time detection in streaming data, result
interpretability, etc. In this paper, we develop a novel deep architecture to
properly extract the baseline from time series, namely Deep Baseline Network
(DBLN). By using this deep network, we can easily locate the baseline position
and then provide reliable and interpretable anomaly detection result. Empirical
evaluation on both synthetic and public real-world datasets shows that our
purely unsupervised algorithm achieves superior performance compared with
state-of-art methods and has good practical applications.
