Online Time Series Prediction with Missing Data
Abstract
We consider the problem of time series prediction in the presence of missing data. We cast the problem as an online learning problem in which the goal of the learner is to minimize prediction error. We then devise an efficient algorithm for the problem, which is based on autoregressive model, and does not assume any structure on the missing data nor on the mechanism that generates the time series. We show that our algorithm’s performance asymptotically approaches the performance of the best AR predictor in hindsight, and corroborate the theoretic results with an empirical study on synthetic and real-world data.
Cite
Text
Anava et al. "Online Time Series Prediction with Missing Data." International Conference on Machine Learning, 2015.Markdown
[Anava et al. "Online Time Series Prediction with Missing Data." International Conference on Machine Learning, 2015.](https://mlanthology.org/icml/2015/anava2015icml-online/)BibTeX
@inproceedings{anava2015icml-online,
title = {{Online Time Series Prediction with Missing Data}},
author = {Anava, Oren and Hazan, Elad and Zeevi, Assaf},
booktitle = {International Conference on Machine Learning},
year = {2015},
pages = {2191-2199},
volume = {37},
url = {https://mlanthology.org/icml/2015/anava2015icml-online/}
}