Augmented Functional Time Series Representation and Forecasting with Gaussian Processes

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Nicolas Chapados, Yoshua Bengio


We introduce a functional representation of time series which allows forecasts to be performed over an unspeciļ¬ed horizon with progressively-revealed informa- tion 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 con- tracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.