Augmented Functional Time Series Representation and Forecasting with Gaussian Processes

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

Authors

Nicolas Chapados, Yoshua Bengio

Abstract

We introduce a functional representation of time series which allows forecasts to be performed over an unspecified 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.