Learning Theory and Algorithms for Forecasting Non-Stationary Time Series
Abstract
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.
Cite
Text
Kuznetsov and Mohri. "Learning Theory and Algorithms for Forecasting Non-Stationary Time Series." Neural Information Processing Systems, 2015.Markdown
[Kuznetsov and Mohri. "Learning Theory and Algorithms for Forecasting Non-Stationary Time Series." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/kuznetsov2015neurips-learning/)BibTeX
@inproceedings{kuznetsov2015neurips-learning,
title = {{Learning Theory and Algorithms for Forecasting Non-Stationary Time Series}},
author = {Kuznetsov, Vitaly and Mohri, Mehryar},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {541-549},
url = {https://mlanthology.org/neurips/2015/kuznetsov2015neurips-learning/}
}