Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
This work addresses the need for fast and reliable anomaly detection in event-sensitive applications like robotic monitoring and data center security, though it is incremental in improving existing unsupervised methods.
The authors tackled the problem of real-time anomaly detection in time series with complex seasonality by developing an unsupervised scheme using a novel Local Trend Inconsistency metric, which outperformed existing methods in AUC scores while maintaining efficiency.
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.