Oral Session: Learning Theory and Algorithms for Forecasting Non-stationary Time Series

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.

Vitaly Kuznetsov
    • Portrait of Ryan Spickard

      Ryan Spickard