Title: Irregularity Reflection Neural Network for Time Series Forecasting. Abstract: Time series forecasting is a long-standing challenge in a variety of industries, and deep learning stands as the mainstream paradigm for handling this forecasting problem. With recent success, representations of time series components (e.g., trend and seasonality) are also considered in the learning process of the models. However, the residual remains under explored due to difficulty in formulating its inherent complexity. In this study, we propose a novel Irregularity Reflection Neural Network (IRN) that reflect the residual for the time series forecasting. First, we redefine the residual as the irregularity and express it as a sum of individual, short regular waves considering the Fourier series in a micro perspective. Second, we design a module, based on the convolutional architectures to mimic the variables of the derived irregularity representation, named Irregularity Representation Block (IRB). IRN comprises IRB on top of a forecasting model to learn the irregularity representation of time series. Extensive experiments on multiple real-world datasets demonstrate that IRN outperforms the state-of-the-art benchmarks in time series forecasting tasks. 