Online time series prediction with missing data

Oren Anava, Elad Hazan, Assaf Zeevi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

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.

Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
EditorsFrancis Bach, David Blei
PublisherInternational Machine Learning Society (IMLS)
Pages2181-2189
Number of pages9
ISBN (Electronic)9781510810587
StatePublished - Jan 1 2015
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: Jul 6 2015Jul 11 2015

Publication series

Name32nd International Conference on Machine Learning, ICML 2015
Volume3

Other

Other32nd International Conference on Machine Learning, ICML 2015
CountryFrance
CityLile
Period7/6/157/11/15

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Science Applications

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  • Cite this

    Anava, O., Hazan, E., & Zeevi, A. (2015). Online time series prediction with missing data. In F. Bach, & D. Blei (Eds.), 32nd International Conference on Machine Learning, ICML 2015 (pp. 2181-2189). (32nd International Conference on Machine Learning, ICML 2015; Vol. 3). International Machine Learning Society (IMLS).