Sanquer, M., Chatelain, F., El-Guedri, M., and Martin, N. A smooth transition model for multiple-regime time series. IEEE transactions on signal processing, 61(7):1835-1847, 2012.
Schäfl, B., Gruber, L., Bitto-Nemling, A., and Hochreiter, S. Hopular: Modern Hopfield Networks for tabular data. arXiv preprint arXiv:2206.00664, 2022.
Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., and Shelby, J. The national solar radiation data base (NSRDB). Renewable and sustainable energy reviews, 89:51-60, 2018.
Smyl, S. A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1):75-85, 2020.
Stankeviciute, K., M Alaa, A., and van der Schaar, M. Conformal time-series forecasting. Advances in Neural Information Processing Systems, 34:6216-6228, 2021.
Sun, S. and Yu, R. Copula conformal prediction for multi-step time series forecasting. ArXiv, abs/2212.03281, 2022.
Sun, X., Kim, S., and Choi, J.-I. Recurrent neural network-induced gaussian process. Neurocomputing, 509:75-84, 2022.
Tagasovska, N. and Lopez-Paz, D. Single-model uncertainties for deep learning. Advances in Neural Information Processing Systems, 32, 2019.
Tajeuna, E. G., Bouguessa, M., and Wang, S. Modeling regime shifts in multiple time series. arXiv preprint arXiv:2109.09692, 2021.
Teng, J., Wen, C., Zhang, D., Bengio, Y., Gao, Y., and Yuan, Y. Predictive inference with feature conformal prediction. arXiv preprint arXiv:2210.00173, 2022.
Thornton, P. E., Running, S. W., White, M. A., et al. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of hydrology, 190(3-4):214-251, 1997.
Tibshirani, R. J., Foygel Barber, R., Candes, E. J., and Ramdas, A. Conformal prediction under covariate shift. Advances in Neural Information Processing Systems, 32, 2019.
Toccaceli, P., Nouretdinov, I., and Gammerman, A. Conformal prediction of biological activity of chemical compounds. Annals of Mathematics and Artificial Intelligence, 81(1):105-123, 2017.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (eds.), Advances in Neural Information Processing Systems 30, pp. 5998-6008. Curran Associates, Inc., 2017.
Vovk, V. Conditional validity of inductive conformal predictors. In *Asian conference on machine learning*, pp. 475-490. PMLR, 2012.
Vovk, V., Gammerman, A., and Saunders, C. Machine-learning applications of algorithmic randomness. In Bratko, I. and Dzeroski, S. (eds.), Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pp. 444-453. Morgan Kaufmann, 1999.
Vovk, V., Gammerman, A., and Shafer, G. Algorithmic learning in a random world. Springer Science & Business Media, 2005.
Widrich, M., Schäfl, B., Pavlovic, M., Ramsauer, H., Gruber, L., Holzleitner, M., Brandstetter, J., Sandve, G. K., Greiff, V., Hochreiter, S., and Klambauer, G. Modern Hopfield Networks and attention for immune repertoire classification. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2020.
Winkler, R. L. A decision-theoretic approach to interval estimation. Journal of the American Statistical Association, 67(337):187-191, 1972.