EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System

March 31, 2016 Β· Declared Dead Β· πŸ› Knowledge Discovery and Data Mining

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Authors Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena, Chang-Tien Lu, Anil Vullikanti, Achla Marathe, Kristen Summers, Graham Katz, Andy Doyle, Jaime Arredondo, Dipak K. Gupta, David Mares, Naren Ramakrishnan arXiv ID 1604.00033 Category cs.CY: Computers & Society Cross-listed cs.SI Citations 39 Venue Knowledge Discovery and Data Mining Last Checked 4 months ago
Abstract
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, and lessons learnt from participating in a forecasting tournament including our perspectives on the limits of forecasting and ethical considerations.
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