| Recurrent neural networks for time series forecasting: Current status and future directions H Hewamalage, C Bergmeir, K Bandara International Journal of Forecasting, 2019 | 875 | 2019 |
| Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach K Bandara, C Bergmeir, S Smyl Expert Systems with Applications, 2017 | 387* | 2017 |
| Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach K Bandara, C Bergmeir, S Smyl Expert Systems with Applications 140 (112896), 2019 | 346 | 2019 |
| 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 Proceedings of the 2019 International Conference on Neural Information …, 2019 | 193 | 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, 2020 | 164 | 2020 |
| Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation K Bandara, H Hewamalage, YH Liu, Y Kang, C Bergmeir Pattern Recognition, 2021 | 118 | 2021 |
| MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns K Bandara, RJ Hyndman, C Bergmeir International Journal of Operational Research, 2021 | 75 | 2021 |
| Global models for time series forecasting: A simulation study H Hewamalage, C Bergmeir, K Bandara Pattern Recognition, 108441, 2021 | 54 | 2021 |
| Ensembles of localised models for time series forecasting R Godahewa, K Bandara, GI Webb, S Smyl, C Bergmeir Knowledge-Based Systems 233, 107518, 2021 | 33 | 2021 |
| Commentary on the M5 forecasting competition S Kolassa International Journal of Forecasting 38 (4), 1562-1568, 2022 | 21* | 2022 |
| The Importance of Environmental Factors in Forecasting Australian Power Demand A Eshragh, B Ganim, T Perkins, K Bandara Environmental Modeling & Assessment, 2020 | 19 | 2020 |
| Multi-resolution, multi-horizon distributed solar PV power forecasting with forecast combinations M Perera, J De Hoog, K Bandara, S Halgamuge Expert Systems with Applications 205, 117690, 2022 | 17 | 2022 |
| Towards Accurate Predictions and Causal'What-if'Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand K Bandara, C Bergmeir, S Campbell, D Scott, D Lubman Proceedings of the 2020 International Joint Conference on Neural Networks, 2020 | 17 | 2020 |
| Insights into the accuracy of social scientists’ forecasts of societal change Nature human behaviour 7 (4), 484-501, 2023 | 13 | 2023 |
| Can machine learning improve small area population forecasts? A forecast combination approach I Grossman, K Bandara, T Wilson, M Kirley Computers, Environment and Urban Systems 95, 101806, 2022 | 11 | 2022 |
| Recurrent neural networks for time series forecasting: Current status and future directions. arXiv 2019 H Hewamalage, C Bergmeir, K Bandara International Journal of Forecasting, 0 | 11 | |
| Study of planetary boundary layer, air pollution, air quality models and aerosol transport using ceilometers in New South Wales (NSW), Australia HN Duc, MM Rahman, T Trieu, M Azzi, M Riley, T Koh, S Liu, K Bandara, ... Atmosphere 13 (2), 176, 2022 | 8 | 2022 |
| Handling concept drift in global time series forecasting Z Liu, R Godahewa, K Bandara, C Bergmeir Forecasting with Artificial Intelligence: Theory and Applications, 163-189, 2023 | 6 | 2023 |
| Causal Inference Using Global Forecasting Models for Counterfactual Prediction. P Grecov, K Bandara, C Bergmeir, K Ackermann, S Campbell, D Scott, ... PAKDD (2), 282-294, 2021 | 4 | 2021 |
| A Scalable Ensemble of Global and Local Models for Long-term Energy Demand Forecasting. K Bandara, H Hewamalage, R Godahewa International Symposium on Forecasting, 2021 | 3 | 2021 |
Christoph BergmeirUniversity of Granada在 monash.edu 的电子邮件经过验证
