  On-line detection of anomalies in time series is a key technique used in
various event-sensitive scenarios such as robotic system monitoring, smart
sensor networks and data center security. However, the increasing diversity of
data sources and the variety of demands make this task more challenging than
ever. Firstly, the rapid increase in unlabeled data means supervised learning
is becoming less suitable in many cases. Secondly, a large portion of time
series data have complex seasonality features. Thirdly, on-line anomaly
detection needs to be fast and reliable. In light of this, we have developed a
prediction-driven, unsupervised anomaly detection scheme, which adopts a
backbone model combining the decomposition and the inference of time series
data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and
an efficient detection algorithm that computes LTI in a real-time manner and
scores each data point robustly in terms of its probability of being anomalous.
We have conducted extensive experimentation to evaluate our algorithm with
several datasets from both public repositories and production environments. The
experimental results show that our scheme outperforms existing representative
anomaly detection algorithms in terms of the commonly used metric, Area Under
Curve (AUC), while achieving the desired efficiency.
