Real-time Social Internet Data to Guide Forecasting Models
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematical approaches and heterogeneous data streams.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE; National Inst. of Health (NIH) (United States)
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1325660
- Report Number(s):
- LA-UR-16-27157
- Country of Publication:
- United States
- Language:
- English
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