Online Learning for Time Series Prediction
Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:172-184, 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, \emphwithout 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.