  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 also also provide novel analysis of stable time series
forecasting algorithm using this new notion of discrepancy that we introduce.
We use our learning bounds to devise new algorithms for non-stationary time
series forecasting for which we report some preliminary experimental results.
