As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
In this paper, we propose the Harmonic Recurrent Process (HRP) for forecasting non-stationary time series with period-varying patterns. HRP works by selectively ensembling recurrent period-varying patterns in harmonic analysis. In contrast to classical forecasting approaches that rely on stationary priors and recurrent neural network approaches that are mostly black boxes, our model is able to deal with irregular nonstationary signals, and its working mechanism is reasonably lucid. We also prove that the stochastic process led by HRP under weak dependence condition is predictive PAC learnable. Comprehensive experiments on simulated and practical tasks validate the effectiveness of HRP.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.