| Recurrent neural networks for time series forecasting: Current status and future directions H Hewamalage, C Bergmeir, K Bandara International Journal of Forecasting, 2020 | 1240 | 2020 |
| Sales demand forecast in e-commerce using a long short-term memory neural network methodology K Bandara, P Shi, C Bergmeir, H Hewamalage, Q Tran, B Seaman Neural Information Processing: 26th International Conference, ICONIP 2019 …, 2019 | 224 | 2019 |
| LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns K Bandara, C Bergmeir, H Hewamalage IEEE transactions on neural networks and learning systems 32 (4), 1586-1599, 2020 | 200 | 2020 |
| Neuralprophet: Explainable forecasting at scale O Triebe, H Hewamalage, P Pilyugina, N Laptev, C Bergmeir, ... arXiv preprint arXiv:2111.15397, 2021 | 172 | 2021 |
| Improving the accuracy of global forecasting models using time series data augmentation K Bandara, H Hewamalage, YH Liu, Y Kang, C Bergmeir Pattern Recognition 120, 108148, 2021 | 149 | 2021 |
| Forecast evaluation for data scientists: common pitfalls and best practices H Hewamalage, K Ackermann, C Bergmeir Data Mining and Knowledge Discovery 37 (2), 788-832, 2023 | 127 | 2023 |
| Global models for time series forecasting: A simulation study H Hewamalage, C Bergmeir, K Bandara Pattern Recognition 124, 108441, 2022 | 81 | 2022 |
| A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition K Bandara, H Hewamalage, R Godahewa, P Gamakumara International Journal of Forecasting 38 (4), 1400-1404, 2022 | 7 | 2022 |
| Deep learning approaches for long-term global horizontal irradiance forecasting for microgrids planning AA Medina-Santana, H Hewamalage, LE Cárdenas-Barrón Designs 6 (5), 83, 2022 | 7 | 2022 |
| A look at the evaluation setup of the m5 forecasting competition H Hewamalage, P Montero-Manso, C Bergmeir, RJ Hyndman arXiv preprint arXiv:2108.03588, 2021 | 7 | 2021 |
| Evaluation of feature-based object identification for augmented reality applications on mobile devices B Hettige, H Hewamalage, C Rajapaksha, N Wajirasena, A Pemasiri, ... 2015 IEEE 10th International Conference on Industrial and Information …, 2015 | 7 | 2015 |
| The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond D Pekaslan, JM Alonso-Moral, K Bandara, C Bergmeir, ... arXiv preprint arXiv:2311.04007, 2023 | | 2023 |
| Advancing Time Series Forecasting Techniques & Practices in a Big Data Environment HP HEWAMALAGE Monash University, 2022 | | 2022 |
| Global models for time series forecasting: A simulation study H Hewamalage, C Bergmeir, K Bandara 40th International Symposium on Forecasting, 2020 | | 2020 |
| Recurrent Neural Networks for Time Series Forecasting: An Overview and Empirical Evaluations H Hewamalage, C Bergmeir, K Bandara 39th International Symposium on Forecasting, Thessaloniki, Greece, 2019 | | 2019 |
| Forecasting Hierarchical Time Series Using Non-Linear Mappings S Wickramasuriya, K Bandara, H Hewamalage, M Perera Available at SSRN 4793559, 0 | | |
Christoph BergmeirUniversity of GranadaPatvirtintas el. paštas monash.edu