Predictive Modeling

Exclusive Event

The web continues to reach into new corners of our lives, becoming a mirror of ourselves as crowds.

Google's search term analysis reveals the unseen spread of influenza and Twitter analysis reveals the latest opinion trends. Recorded Future's Temporal Analytics™ Engine transforms public web sources into temporal events: a real-time window into the events the world is reporting and talking about, structured for expert analysis.

Empowered by this data, analysts use Recorded Future to make forecasts of violent unrest and cyber attacks around the world.

This on-demand webinar with MIT Researcher, Nathan Kallus demonstrates how he created such forecasts automatically with quantitative models.

After watching this presentation, you'll understand how Nathan:

  • Applied machine-learning methods to Recorded Future data.
  • Developed models that accurately predict significant protests by location.
  • Developed models that accurately predict cyber attacks by target or perpetrator.

For example, these models forecast weeks in advance both the expected unrest on the anniversary of ousted Egyptian president Morsi's rise to power and the unexpected outcomes in the days that followed.

These techniques can extend the web from a global mirror to a crystal ball for the future of volatile locales.

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