  Identifying anomalous patterns in real-world data is essential for
understanding where, when, and how systems deviate from their expected
dynamics. Yet methods that separately consider the anomalousness of each
individual data point have low detection power for subtle, emerging
irregularities. Additionally, recent detection techniques based on subset
scanning make strong independence assumptions and suffer degraded performance
in correlated data. We introduce methods for identifying anomalous patterns in
non-iid data by combining Gaussian processes with novel log-likelihood ratio
statistic and subset scanning techniques. Our approaches are powerful,
interpretable, and can integrate information across multiple data streams. We
illustrate their performance on numeric simulations and three open source
spatiotemporal datasets of opioid overdose deaths, 311 calls, and storm
reports.
