EMBERS at 4 years:Experiences operating an Open Source Indicators Forecasting System
Sathappan Muthiah*, Virginia Tech; Naren Ramakrishnan, Virginia Tech; Patrick Butler, Virginia Tech; Rupinder Khandpur, Virginia Tech; PARANG SARAF, VIRGINIA TECH; Anil Vullikanti, Virginia Tech; Achla Marathe, Virginia Tech; Graham Katz, CACI; Andrew Doyle, CACI; Jaime Arredondo, UCSD; Dipak Gupta, SDSU; David Mares, UCSD; Jose Cadena, Virginia Tech; Liang Zhao, VT; Nathan Self, ; Alla Rozovskaya, Virginia Tech; Kristen Summers, IBM
EMBERS is an anticipatory intelligence system forecasting population-level events in multiple countries of Latin America. A deployed system from 2012, EMBERS has been generating alerts 24x7 by ingesting a broad range of data sources including news, blogs, tweets, machine coded events, currency rates, and food prices. In this paper, we describe our experiences operating EMBERS continuously for nearly 4 years, with speciﬁc attention to the discoveries it has enabled, correct as well as missed forecasts, lessons learnt from participating in a forecasting tournament, and our perspectives on the limits of forecasting including ethical considerations.
Filed under: Big Data