The microprediction python client README explains how publishing numbers repeatedly initiates a fight between algorithms that watch microprediction.org. If you'd like these algorithms to serve you, the create_a_stream.py example might be the fastest way to get going. Here's a video demonstrating creation of a data stream in under ten minutes. Once this is done, you can sit back and wait for the algorithms to arrive. There are numerous benefits to this approach.
No sales folks to talk to, or email to surrender. Just use the Python client or call our API.
A swarm of fiercely competing algorithms can try to predict your model errors.
Algorithms can utilize existing streams or exogenous data.
Predictions improve as new data, models and talent become available.
A key use for this public prediction API is the ongoing performance analysis of private models used on private data. In this pattern, you publish the difference between your prediction model and the revealed truth. Have you ever wondered?
Finding capable data scientists today is difficult. By seeking predictions that fit your organization’s needs, Microprediction can help you uncover the kind of talent you’re seeking. Every Microprediction challenge has its own leaderboard which can help filter contributors with the most appropriate skill sets for your organization.
Offering reward money to get predictions has never been easier or more economical. You can offer compensation directly linked to your data streams. Join leading financial institutions who have discovered how easy this is. Our platform is programmatically self-governed, so you get efficient access to high-quality predictions on the cheap.
Avoid weeks of planning and setup time – one call to our API gets you started.
No agency costs means you can allocate higher incentives to attract talent.
Reward mechanisms allow for more contributors to improve accuracy.
Microprediction systematically evaluates and combines competing prediction models against your data for four time horizons:
You’ll be able to retrieve the following:
See An Introduction to Z-Streams for explanation of the mechanics, and Tears of Joy, our blog article illustrating how useful z-streams can be. The swarming algorithms don’t just predict your data. They also predict how other algorithms will predict your data -- feedback for more accurate predictions!