[1302.6927] Online Learning for Time Series Prediction
source link: https://arxiv.org/abs/1302.6927
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[Submitted on 27 Feb 2013]
Online Learning for Time Series Prediction
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.
| Comments: | 17 pages, 6 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:1302.6927 [cs.LG] |
| (or arXiv:1302.6927v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.1302.6927 |
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