We present online prediction methods for time series that let us explicitly handle nonstationary artifacts (e.g., trend and seasonality) present in most real time series. Specifically, we show that applying appropriate transformations to such time series before prediction can lead to improved theoretical and empirical prediction performance. Moreover, since these transformations are usually unknown, we employ the learning with experts setting to develop a fully online method (NonSTOP-NonSTationary Online Prediction) for predicting nonstationary time series. This framework allows for seasonality and\/or other trends in univariate time series and cointegration in multivariate time series. Our algorithms and regret analysis subsume recent related work while significantly expanding the applicability of such methods. For all the methods, we provide sublinear regret bounds. We support all of our results with experiments on simulated and real data.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present online prediction methods for time series that let us explicitly handle nonstationary artifacts (e.g., trend and seasonality) present in most real time series. Specifically, we show that applying appropriate transformations to such time series before prediction can lead to improved theoretical and empirical prediction performance. Moreover, since these transformations are usually unknown, we 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