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Can you build the best system to automatically forecast global news events?
The Challenge has ended. See results below.
The Challenge
Develop and test innovative forecasting methods that ingest and process publicly available data sources to predict events in the Middle East and North Africa
Final Results
Win from a prize pool of $100,000 for the best eligible solutions


The Problem

In an effort to provide early warning capabilities, the Department of Defense’s Integrated Crisis Early Warning System (ICEWS) and IARPA’s Open Source Indicators (OSI) programs want to leverage novel statistical and machine learning techniques using publicly available data sources to forecast societal such as civil unrest and disease outbreaks with a high degree of accuracy.

Participants are encouraged to develop and test innovative forecasting methods that ingest and process publicly available data sources to predict Military Activity, Non-violent Civil Unrest, and Infectious Disease in specific places of interest.

Participant forecasts will be scored across a three-month rolling window. Every month, IARPA will score those participants who have sent forecasts for three consecutive months. Scoring will be based on four metrics including forecast lead time, location accuracy, date accuracy, and facet actor/event-type matching. This challenge is scheduled to run from July 2018 until early 2019 when winners are announced.

Technologists, data scientists, and machine learning engineers who are skilled at breaking down complex data are encouraged to join. Individuals ranging from private industry and academia are all eligible to participate and win prizes. The Mercury Challenge Team believes success in this challenge can prove to be a strong addition to any data science practitioner’s portfolio.

Challenge Details


The Mercury Challenge seeks innovative solutions and methods for the automated generation of event forecasts in the Middle East and North Africa.

The three specific event classes of interest are:

  1. Military Activity (MA) in Iraq and Syria:
    • Conflict – Incident where police, military, or other state/government security forces take action in some way; and
    • Force posture – A newsworthy action of police, military, or other state/government security forces that does not involve the use of deadly force.
  2. Non-violent Civil Unrest (CU) in Egypt and Jordan, such as demonstrations, marches and protests:
    • Daily count of non-violent civil unrest events in Egypt
    • Weekly count of non-violent civil unrest events in Jordan

  3. Infectious disease in Saudi Arabia: Weekly MERS count.


Period 1

Period 2

Congratulations to the winners of the Challenge!

Read the full Prize Calculation Documentation for all the details on prizes.

Period 1 Prize Purse

Period 2 Prize Purse

Best Undergrad

Total Prizes


Rules & Eligibility

To be eligible to win a prize under this competition, an individual or entity:

  1. Must be (1) an individual or team each of whom are 18 years of age and over, or (2) an entity incorporated; and
  2. May not be a federal entity or federal employee acting within the scope of their employment. An individual or entity shall not be deemed ineligible because the individual or entity used federal facilities or consulted with federal employees during a competition if the facilities and employees are made available to all individuals and entities participating in the competition on an equitable basis.

Federal grantees may not use federal funds to develop challenge solutions unless consistent with the purpose of their grant award. Federal contractors may not use federal funds from a contract to develop challenge applications or to fund efforts in support of a challenge submission.

Please read the full Challenge Rules documentation for additional rules and eligibility requirements.


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The Intelligence Advanced Research Projects Activity (IARPA) invests in high-risk, high-payoff research programs to tackle some of the most difficult challenges of the agencies and disciplines in the Intelligence Community (IC).