Learning Theory and Algorithms for Forecasting Non-Stationary Time Series

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》资源》论文》Learning Theory and Algorithms for Forecasting Non-Stationary Time Series

Learning Theory and Algorithms for Forecasting Non-Stationary Time Series

2020-02-04 |

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Learning Theory and Algorithms for Forecasting Non-Stationary Time Series
论文

Abstract

We present data-dependent learning bounds for the general scenario of nonstationary 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.

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