Knowledge Center

Video Introductions, Examples and Structured Learning

Welcome to the prediction network.

To motivate this project, here's a first look and some use cases to get you thinking about the potential for an open, democratic prediction network. If nothing else it automates benchmarking, and offers a new way to defining time-series anomaly, but it might also bring AI within the reach of individuals and small business some day.

To that end we produce open-source code that and is fast and simple, and intended to foster collective AI at web scale. We run daily and monthly competitions, and we're also sponsoring the M6 Financial Forecasting Competition that will run throughout 2022 with $300,000 in prizes. We have a bit of fun with this, so join our slack channel.

Join Our Slack Channel

Documentation

See the documentation for minimalist instructions on creating streams and submitting predictions. 

FAQ?

It moved to Frequently asked questions. Raise an issue on Github.


Python Notebook Examples

The repository contains notebook examples you can easily run (for example just open them in Google Colab).

and more. 

See the documentation

 


R-Tutorials  

Welcome R statisticians! 

More on the way soon. 


Julia Examples  

Welcome, Julia developers!  Video tutorials are on the way. For now:

More at Rusty's repo including an advanced electricity prediction project


Player Strategy

Be sure you understand the mechanics of how predictions are quarantined and rewarded.


Business Context

For those who may be interested, and willing to invest the time, here is a sequence of talks providing what is hopefully a coherent case for an open prediction network. It isn't an elevator pitch.

Along the way, we explain why vendor Automated Machine Learning and previous attempts at crowdsourcing might violate a fundamental axiom, at least if quantitative business optimization is to be fully democratized. Pro tip: Some people like to start with #3. 

  1. A first look at microprediction and how it gives rise to the "ten minute data science project". 
  2. What must a microprediction oracle do?  The answer isn't Kaggle, or DataRobot. 
  3. Business uses of microprediction include pretty much everything (unless "prediction" is in the name, ironically). 
  4. Repeated value function prediction provides the link between microprediction and Control Theory and Reinforcement Learning, which is why microprediction is truly general. 
  5. Why algorithms make excellent managers, and by implication why humans are terrible. Copious theory means algorithms are well placed to orchestrate production of prediction. Humans need not occupy a blocking role.

Learn Data Science

If one person decides to learn to code so they can use the prediction network, we're done. Seriously it isn't that hard. Here are some entirely free ways to get going.

  1. Visual Coding and games to get started with coding concepts (my daughter and I love some of these).
  2. Learn to Code, in Python if you've never coded before.
  3. Learn Python more quickly if you've coded in another language. 
  4. Learn data science using Python. 

Suggestions

Please contact us if you can help improve these resources. As noted above we'd love to answer your questions via Gitter, or issues on Github, the discussion on GitHub, or even questions at Quora. You can also email us directly. 

Stay Informed

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