[17] Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. Adaptive graph convolutional recurrent network for traffic forecasting. In NeurIPS, 2020.
[18] Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. In ICLR, 2020.
[19] Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. N-beats: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437, 2019.
[20] Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, and Jian Li. Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures. arXiv preprint arXiv:2207.01186, 2022.
[21] Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series forecasting? arXiv preprint arXiv:2205.13504, 2022.
[22] Duraisamy Sundararajan. The discrete Fourier transform: theory, algorithms and applications. World Scientific, 2001.
[23] Mark W. Watson. Vector autoregressions and cointegration. Working Paper Series, Macroeconomic Issues, 4, 1993.
[24] Dimitros Asteriou and Stephen G Hall. Arima models and the box-jenkins methodology. Applied Econometrics, 2(2):265-286, 2011.
[25] Bryan Lim and Stefan Zohren. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194):20200209, feb 2021.
[26] José Torres, Dalil Hadjout, Abderrazak Sebaa, Francisco Martínez-Álvarez, and Alicia Troncoso. Deep learning for time series forecasting: A survey. *Big Data*, 9, 12 2020.
[27] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. Graph wavenet for deep spatial-temporal graph modeling. In IJCAI, pages 1907-1913, 2019.
[28] Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, and Hui Xiong. A survey on deep learning based time series analysis with frequency transformation. CoRR, abs/2302.02173, 2023.
[29] Liheng Zhang, Charu C. Aggarwal, and Guo-Jun Qi. Stock price prediction via discovering multi-frequency trading patterns. In KDD, pages 2141-2149, 2017.
[30] Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. In ICML, 2022.
[31] Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshit Kumar, and Steven C. H. Hoi. Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting. In ICLR. OpenReview.net, 2022.
[32] Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, and Rong Jin. Film: Frequency improved legendre memory model for long-term time series forecasting. 2022.
[33] Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, and Tie-Yan Liu. DEPTS: deep expansion learning for periodic time series forecasting. In ICLR. OpenReview.net, 2022.
[34] Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler, and Artur Dubrawski. N-hits: Neural hierarchical interpolation for time series forecasting. CoRR, abs/2201.12886, 2022.
[35] Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series forecasting? 2023.