[1] 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.
[2] Cristian Challu, Kin G Olivares, Boris N Oreshkin, Federico Garza, Max Mergenthaler, and Artur Dubrawski. N-hits: Neural hierarchical interpolation for time series forecasting. arXiv preprint arXiv:2201.12886, 2022.
[3] Tian Zhou, Ziqing Ma, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin, et al. Film: Frequency improved legendre memory model for long-term time series forecasting. Advances in Neural Information Processing Systems, 35:12677-12690, 2022.
[4] Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series forecasting? arXiv preprint arXiv:2205.13504, 2022.
[5] Shaojie Bai, J Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018.
[6] David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. Deeper: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3):1181-1191, 2020.
[7] Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11106-11115, 2021.
[8] Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, 34:22419-22430, 2021.
[9] Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. arXiv preprint arXiv:2201.12740, 2022.
[10] Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long. Non-stationary transformers: Rethinking the stationarity in time series forecasting. arXiv preprint arXiv:2205.14415, 2022.
[11] Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems, 32, 2019.
[12] Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451, 2020.
[13] Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, and Schahram Dustdar. Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In International Conference on Learning Representations, 2021.
[14] Romain Cosentino and Behnaam Aazhang. Learnable group transform for time-series. In International conference on machine learning, pages 2164-2173. PMLR, 2020.
[15] Randall Balestriero, Hervé Glotin, and Richard Baranuik. Interpretable and learnable superresolution time-frequency representation. In Mathematical and Scientific Machine Learning, pages 118-152. PMLR, 2022.
[16] Hendrik Purwins, Bo Li, Tuomas Virtanen, Jan Schlüter, Shuo-Yiin Chang, and Tara Sainath. Deep learning for audio signal processing. IEEE Journal of Selected Topics in Signal Processing, 13(2):206-219, 2019.
[17] Stefan Lattner, Monika Dörfler, and Andreas Arzt. Learning complex basis functions for invariant representations of audio. arXiv preprint arXiv:1907.05982, 2019.