Learning Theory and Algorithms for Forecasting Non-stationary Time Series

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. Papers published at the Neural Information Processing Systems Conference.