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arXiv · 1302.6927

Online Learning for Time Series Prediction

Oren Anava·Elad Hazan·Shie Mannor·Ohad Shamir
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Abstract

In this paper 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.

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Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir. 2013-02-27. Online Learning for Time Series Prediction. https://arxiv.org/abs/1302.6927

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