[26] Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. In Proceedings of the International Conference on Learning Representations (ICLR), 2020.
[27] Bjoern Krollner, Bruce J Vanstone, Gavin R Finnie, et al. Financial time series forecasting with machine learning techniques: a survey. In *ESANN*, 2010.
[28] Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. Modeling long-and short-term temporal patterns with deep neural networks. In The 41st international ACM SIGIR conference on research & development in information retrieval, pages 95-104, 2018.
[29] Bryan Lim and Stefan Zohren. Time-series forecasting with deep learning: a survey. *Philosophical Transactions of the Royal Society A*, 379(2194):20200209, 2021.
[30] Amal Mahmoud and Ammar Mohammed. A survey on deep learning for time-series forecasting. Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges, pages 365-392, 2021.
[31] Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1):54-74, 2020.
[32] Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Ganesh Ramakrishnan, Vishak Prasad, Micah Goldblum, and Colin White. When do neural nets outperform boosted trees on tabular data? In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.
[33] Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. Transformers can do bayesian inference. In Proceedings of the International Conference on Learning Representations (ICLR), 2022.
[34] Maria Elena Nenni, Luca Giustiniano, and Luca Pirolo. Demand forecasting in the fashion industry: a review. International Journal of Engineering Business Management, 5(Godiste 2013):5-36, 2013.
[35] 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.
[36] Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. Meta-learning framework with applications to zero-shot time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021.
[37] Bernardo Pérez Orozco and Stephen J Roberts. Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks. arXiv preprint arXiv:2003.12162, 2020.
[38] Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al. Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214, 2022.
[39] Caltrans PeMS. Caltrans performance measurement system (pems). US DOT, 2021.
[40] Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. Deep state space models for time series forecasting. Advances in neural information processing systems, 31, 2018.
[41] David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. Deeper: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 2020.
[42] Omer Berat Sezer, Mehmet Ugur Gudelek, and Ahmet Murat Ozbayoglu. Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied soft computing, 90:106181, 2020.
[43] Taylor G. Smith et al. pmdarima: Arima estimators for Python, 2017-. [Online; accessed <today>].