In a new kind of prediction network, self-navigating Python, R and Julia algorithms conspire to produce superior electricity predictions than the official forecasts - then automatically review each other’s model residuals. They also find their way to any published time series, thereby providing essentially free prediction to anyone who needs it. I will discuss the potential for collective real-time prediction, and demonstrate a prototypical host at Microprediction.org. Parts of contest theory and a lottery paradox are highly relevant to algorithms submitting distributional predictions.
Dr. Cotton heads Intech’s data science efforts in collaboration with the investment team, and is also the primary developer of open source software supporting a prediction network. In a prior role at JPMorgan he was responsible for advances in algorithmic trading, privacy preserving analytics and crowdsourcing. 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.