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Yue Hu and Siwei Yu are with the School of Mathematics, Harbin Institute of Technology, Harbin 150001, China (e-mail: yuehu@stu.hit.edu.cn; siweiyu@hit.edu.cn). Jialiang Tang is with the School of Computer Science and Engineering, Nanjing University of Science and Technology, China. Baosheng Yu is with the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. Jing Zhang is with the School of Computer Science, Wuhan University, Wuhan, China. Dacheng Tao is with the College of Computing and Data Science, Nanyang Technological University, Singapore.(Yue Hu and Jialiang Tang contributed equally to this work. Corresponding authors: Siwei Yu and Baosheng Yu.)Code is available at https://github.com/y563642-max/timeapn
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By explicitly modeling and predicting non-stationary factors in both time and frequency domains, TimeAPN significantly boosts the accuracy of long-term time series forecasting, outperforming existing normalization techniques.
