Vuilleumier, L. (2018). Meteorological synoptical observations from station Payerne (2015-01). Backup Publisher: Swiss Meteorological Agency, Payerne Type: data set.
Wang, H., Lei, Z., Zhang, X., Zhou, B., and Peng, J. (2019a). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198:111799.
Wang, H., Lei, Z., Zhang, X., Zhou, B., and Peng, J. (2019b). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198:111799.
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., and Sun, L. (2022). Transformers in time series: A survey. arXiv preprint arXiv:2202.07125.
Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M. (2023). Timesnet: Temporal 2d-variation modeling for general time series analysis. In International Conference on Learning Representations.
Wu, H., Xu, J., Wang, J., and Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, 34:22419-22430.
Yagli, G. M., Yang, D., and Srinivasan, D. (2020). Ensemble solar forecasting using data-driven models with probabilistic post-processing through GAMLSS. Solar Energy, 208:612-622.
Yang, D., Wang, W., Gueymard, C. A., Hong, T., Kleissl, J., Huang, J., Perez, M. J., Perez, R., Bright, J. M., Xia, X., van der Meer, D., and Peters, I. M. (2022). A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality. Renewable and Sustainable Energy Reviews, 161:112348.
Zelikman, E., Zhou, S., Irvin, J., Raterink, C., Sheng, H., Avati, A., Kelly, J., Rajagopal, R., Ng, A. Y., and Gagne, D. (2020). Short-term solar irradiance forecasting using calibrated probabilistic models. arXiv preprint arXiv: 2010.04715.
Zeng, A., Chen, M.-H., Zhang, L., and Xu, Q. (2022). Are transformers effective for time series forecasting? ARXIV.ORG.
Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., and Li, J. (2022). Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures.
Zhang, X., Zhen, Z., Sun, Y., Wang, F., Zhang, Y., Ren, H., Ma, H., and Zhang, W. (2023). Prediction interval estimation and deterministic forecasting model using ground-based sky image. IEEE Transactions on Industry Applications, 59(2):2210-2224.
Zhang, Y. and Yan, J. (2023). Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The Eleventh International Conference on Learning Representations.
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021). Informer: Beyond efficient transformer for long sequence time-series forecasting. In The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Conference, volume 35, pages 11106-11115. AAAI Press.
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R. (2022a). Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.
Zhou, T., Ma, Z., xue wang, Wen, Q., Sun, L., Yao, T., Yin, W., and Jin, R. (2022b). FiLM: Frequency improved legendre memory model for long-term time series forecasting. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K., editors, Advances in Neural Information Processing Systems.