1. Multivariate time series prediction by RNN architectures for energy consumption forecasting;Amalou;Energy Reports,2022
2. Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning;Cannas;Physics and Chemistry of the Earth, Parts A/B/C,2006
3. Chung, Y., Neiswanger, W., Char, I., & Schneider, J. (2021). Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. In Advances in neural information processing systems: annual conference on neural information processing systems (pp. 10971–10984). Canada.
4. Trustworthy uncertainty propagation for sequential time-series analysis in RNNs;Dera;IEEE Transactions on Knowledge and Data Engineering,2024
5. Application of empirical mode decomposition and Hodrick Prescot filter for the prediction single step and multistep significant wave height with LSTM;Domala;Ocean Engineering,2023