Paper Info
Title
Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality
Abstract
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. First, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Second, a large portion of time series data have complex seasonality features. Third, 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.
Year
DOI
Venue
2022
10.1109/TKDE.2020.3035685
IEEE Transactions on Knowledge and Data Engineering
Keywords
DocType
Volume
Time series,seasonality,anomaly detection,unsupervised learning
Journal
34
Issue
ISSN
Citations 
9
1041-4347
0
PageRank 
References 
Authors
0.34
26
7
Name
Order
Citations
PageRank
Wentai Wu100.34
Ligang He254256.73
Weiwei Lin314312.22
Yi Su400.34
Yuhua Cui500.34
Carsten Maple660385.70
Stephen A. Jarvis7107387.04