Harmonic Recurrent Process for Time Series Forecasting

Shao-Qun Zhang, Zhi-Hua Zhou

Abstract: 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.