Gaussian process subset scanning for anomalous pattern detection in non-iid data

William Herlands, Edward McFowland, Andrew G. Wilson, Daniel B. Neill

Research output: Contribution to conferencePaper

Abstract

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.

Original languageEnglish (US)
Pages425-434
Number of pages10
StatePublished - Jan 1 2018
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: Apr 9 2018Apr 11 2018

Conference

Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
CountrySpain
CityPlaya Blanca, Lanzarote, Canary Islands
Period4/9/184/11/18

ASJC Scopus subject areas

  • Statistics and Probability
  • Artificial Intelligence

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  • Cite this

    Herlands, W., McFowland, E., Wilson, A. G., & Neill, D. B. (2018). Gaussian process subset scanning for anomalous pattern detection in non-iid data. 425-434. Paper presented at 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018, Playa Blanca, Lanzarote, Canary Islands, Spain.