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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