In this paper, we study how to capture explicit periodicity to boost the accuracy of deep models in univariate time series forecasting. Recent advanced deep learning models such as recurrent neural networks (RNNs) and transformers have reached new heights in terms of modeling sequential data, such as natural languages, due to their powerful expressiveness. However, real-world time series are often more
