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Augmented Functional Time Series Representation and Forecasting with Gaussian Processes
Nicolas Chapados · Yoshua Bengio

Wed Dec 05 09:50 AM -- 10:00 AM (PST) @

We introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets. By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available. This information is put to use in an application to actively trade price spreads between commodity futures contracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.

Author Information

Nicolas Chapados (ServiceNow Research)
Nicolas Chapados

Nicolas Chapados is Vice-President, Research at ServiceNow Inc. He holds an engineering degree from McGill University and a PhD in Computer Science from University of Montreal, Canada. While still writing his thesis and jointly with his advisor Yoshua Bengio, he co-founded [ApSTAT Technologies](https://www.apstat.com) in 2001, a machine learning technology transfer firm, to apply cutting-edge academic research ideas to areas such as insurance risk evaluation, supply chain planning, business forecasting, national defence, and hedge fund management. From this work, he also co-founded spin-off companies: Imagia, to detect and quantify cancer early with AI analysis of medical images, Element AI (acquired by ServiceNow in January 2021), and [Chapados Couture Capital](https://www.chapados-couture.com), a quantitative asset manager. He holds the Chartered Financial Analyst (CFA) designation.

Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.

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