Kavli Affiliate: Xian Chen
| First 5 Authors: Xian Chen, Xian Chen, , ,
| Summary:
Fine-grained time series data are crucial for accurate and timely online
change detection. While both collective anomalies and change points can coexist
in such data, their joint online detection has received limited attention. In
this research, we develop a Bayesian framework capturing time series with
collective anomalies and change points, and introduce a recursive online
inference algorithm to detect the most recent collective anomaly and change
point jointly. For scaling, we further propose an algorithm enhanced with
collective anomaly removal that effectively reduces the time and space
complexity to linear. We demonstrate the effectiveness of our approach via
extensive experiments on simulated data and two real-world applications.
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