About Us

Making turnkey predictions available to everyone.

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Democratizing Prediction

We’ve built a network of real-time data and algorithms to make low-cost, high-quality predictions accessible and useful. Getting predictions is as simple as publishing data to our platform where competing, state-of-the art algorithms converge on optimized predictions. Best of all, whether you need predictions or make them, you run the show – no middlemen required.

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What’s Microprediction?

Micropredictions are repeated, very short-term forecasts -- nowcasts -- using real-time data streams. They’re important in many domains, from stock markets to energy consumption to weather. And they have important routine applications as well, such as

  • Cleaning and enriching your live data
  • Alerted you outliers and anomalies
  • Finding patterns in your in-house model’s residuals (i.e. errors)

See the third business module in the knowledge center for a video introduction to the myriad uses of frequently repeated prediction. This includes most things we refer to as Machine Learning or Artificial Intelligence. 

Welcome Module 3

Let’s Change Prediction Together

Microprediction is inherently difficult because it’s an exploration inside the world's data and models. Only a complex network of data scientists – not individuals – can deliver on the promise of Microprediction. Together, we can transform the prediction supply chain for governments, corporations, and scientists from easy access to more profound, collective intelligence.

Project Lead

Peter CottonDr. Peter Cotton, a career quant, entrepreneur and leading authority on crowdsourcing, is the creator of microprediction.com and founder of Micropredictions, a division of Intech Investments. Peter currently leads Intech's data science efforts and is the developer of multiple financial modeling patents.

Before joining Intech®, Dr. Cotton spent six years at JPMorgan, where he served as executive director of data science. He created ROAR, a collective intelligence platform with over 1,000 contributing data scientists within JPMorgan;  also initiating the privacy preserving computation research, and the use of optimal control in trading. Previously, he was the founder of Benchmark Solutions, a company that pioneered large-scale financial data assimilation and was later sold to Bloomberg. Peter began his career at Morgan Stanley where he was one of several independent inventors of closed-form synthetic CDO pricing.

Dr. Cotton earned an undergraduate degree in physics and mathematics from the University of New South Wales and a PhD in mathematics from Stanford University.

Project Contributors

The open source project started by Dr. Cotton has received invaluable assistance from contributors, in particular Eric Lou, a student at Stanford University, and Rusty Conover, a highly experienced technologist who previously built out a backtesting infrastructure for hedge fund Two Sigma.  We encourage anyone interested in the potential for an open prediction network to reach out on Gitter or Github


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