Online Learning for Time Series Prediction
This work addresses time series prediction for applications requiring robust online learning, but it is incremental as it builds on existing regret minimization techniques.
The paper tackles the problem of predicting time series using ARMA models under minimal noise assumptions, developing online learning algorithms that asymptotically approach the performance of the best ARMA model in hindsight.
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.