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Wednesday, December 9 • 09:50 - 10:10
Learning Theory and Algorithms for Forecasting Non-stationary Time Series

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We present data-dependent learning bounds for the general scenario of non-stationary non-mixing stochastic processes. Our learning guarantees are expressed in terms of a data-dependent measure of sequential complexity and a discrepancy measure that can be estimated from data under some mild assumptions. We use our learning bounds to devise new algorithms for non-stationary time series forecasting for which we report some preliminary experimental results.


Wednesday December 9, 2015 09:50 - 10:10
Room 210 A

Attendees (27)