Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
Publications
Publications
  • November 2021
  • Article
  • Proceedings of Machine Learning Research (PMLR)

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data

By: William Herlands, Edward McFowland III, Andrew Gordon Wilson and Daniel B. Neill
  • Format:Print
ShareBar

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 streams. We illustrate their performance on numeric simulations and three open source spatiotemporal datasets of opioid overdose deaths, 311 calls, and storm reports.

Keywords

Pattern Detection; Subset Scanning; Gaussian Processes; Mathematical Methods

Citation

Herlands, William, Edward McFowland III, Andrew Gordon Wilson, and Daniel B. Neill. "Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data." Proceedings of Machine Learning Research (PMLR) 84 (2018): 425–434. (Also presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.)
  • Read Now

About The Author

Edward McFowland III

Technology and Operations Management
→More Publications

More from the Authors

    • March 2025
    • Information and Organization

    Novice Risk Work: How Juniors Coaching Seniors on Emerging Technologies Such as Generative AI Can Lead to Learning Failures

    By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani
    • May 2024
    • Faculty Research

    Pernod Ricard: Uncorking Digital Transformation

    By: Iavor Bojinov, Edward McFowland III, François Candelon, Nikolina Jonsson and Emer Moloney
    • January 2024
    • Bioinformatics

    Subset Scanning for Multi-Trait Analysis Using GWAS Summary Statistics

    By: Rui Cao, Evan Olawsky, Edward McFowland III, Erin Marcotte, Logan Spector and Tianzhong Yang
More from the Authors
  • Novice Risk Work: How Juniors Coaching Seniors on Emerging Technologies Such as Generative AI Can Lead to Learning Failures By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani
  • Pernod Ricard: Uncorking Digital Transformation By: Iavor Bojinov, Edward McFowland III, François Candelon, Nikolina Jonsson and Emer Moloney
  • Subset Scanning for Multi-Trait Analysis Using GWAS Summary Statistics By: Rui Cao, Evan Olawsky, Edward McFowland III, Erin Marcotte, Logan Spector and Tianzhong Yang
ǁ
Campus Map
Harvard Business School
Soldiers Field
Boston, MA 02163
→Map & Directions
→More Contact Information
  • Make a Gift
  • Site Map
  • Jobs
  • Harvard University
  • Trademarks
  • Policies
  • Accessibility
  • Digital Accessibility
Copyright © President & Fellows of Harvard College.