Online learning for time series prediction

Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir

Research output: Contribution to journalConference articlepeer-review

28 Scopus citations

Abstract

We address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show that our algorithm's performances asymptotically approaches the performance of the best ARMA model in hindsight.

Original languageEnglish (US)
Pages (from-to)172-184
Number of pages13
JournalJournal of Machine Learning Research
Volume30
StatePublished - Jan 1 2013
Externally publishedYes
Event26th Conference on Learning Theory, COLT 2013 - Princeton, NJ, United States
Duration: Jun 12 2013Jun 14 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Keywords

  • Online learning
  • Regret minimization
  • Time series analysis

Fingerprint Dive into the research topics of 'Online learning for time series prediction'. Together they form a unique fingerprint.

Cite this