Adverse Event Prediction Using Graph-Augmented Temporal Analysis (Final Report)
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- University of Illinois at Urbana-Champaign, IL (United States)
- University of Maryland, College Park, MD (United States)
- Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
- Missouri University of Science and Technology, Rolla, MO (United States)
- University of San Francisco, CA (United States)
This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to identify precursors providing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication submissions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1481631
- Report Number(s):
- SAND-2018-11123; 669598
- Country of Publication:
- United States
- Language:
- English
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