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Online Learning for Time Series Prediction
This video was recorded at 26th Annual Conference on Learning Theory (COLT), Princeton 2013. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
