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Generative Models for Financial Time-Series Predictions
The intern will work on applying new advances from the field of Machine Learning to models which make predictions about time-series data. The models have the desirable property modeling the distribution of outcomes in a way that we can sample from, allowing us to account for uncertainty in the modelâs predictions. By making more accurate predictions with more accurate gauges of uncertainty, Electronica will be able to construct portfolios which give more desirable risk-adjusted returns to investors.
Faculty Supervisor:
David Duvenaud
Student:
Jonathan Lorraine
Partner:
Electronica AI Inc
Discipline:
Computer science
Sector:
Information and communications technologies
University:
Program:
Accelerate
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