Kasun Bandara

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Christoph BergmeirUniversity of GranadaVerified email at monash.edu
Hansika HewamalageData Scientist, Coles GroupVerified email at coles.com.au
Rakshitha GodahewaForecasting Lead, AusNetVerified email at ausnetservices.com.au
Maneesha PereraHonorary Research Fellow, University of MelbourneVerified email at unimelb.edu.au
Saman K. HalgamugeFIEEE, FIET, FNASSL, Professor, University of Melbourne, Visiting DVC at SLIIT Sri LankaVerified email at unimelb.edu.au
Julian de HoogHonorary Research Fellow, University of MelbourneVerified email at unimelb.edu.au
Peibei ShiMetaVerified email at meta.com
Brian SeamanWalmartlabsVerified email at walmartlabs.com
Quoc TranSmartPricing, WalmartLabsVerified email at walmartlabs.com
Yanfei KangSchool of Economics and Management, Beihang UniversityVerified email at buaa.edu.cn
Michael KirleyProfessor, School of Computing and Information Systems, University of MelbourneVerified email at unimelb.edu.au
Rob J HyndmanProfessor of Statistics, Monash UniversityVerified email at monash.edu
Jeremy SilverUniversity of MelbourneVerified email at unimelb.edu.au
Matthew L. RileyNSW Department of Climate Change, Energy, the Environment and WaterVerified email at environment.nsw.gov.au
Hiep Nguyen DucDepartment of Planning, Industry and Environment NSWVerified email at environment.nsw.gov.au
Prof Dan LubmanTurning Point and Monash UniversityVerified email at monash.edu
Debbie ScottMonash UniversityVerified email at monash.edu
Geoff WebbProfessor, Department of Data Science and Artificial Intelligence, Monash UniversityVerified email at monash.edu
Damith Asanka SenanayakePostdoctoral Research FellowVerified email at unimelb.edu.au
Ali EshraghJohns Hopkins Carey Business SchoolVerified email at jhu.edu

Kasun Bandara

Load Forecast Lead, EnergyAustralia | Honorary Research Fellow, University of Melbourne

Verified email at unimelb.edu.au - Homepage

Machine Learning Time series Forecasting Deep Learning Data Science Artificial Intelligence


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Recurrent neural networks for time series forecasting: Current status and future directions H Hewamalage, C Bergmeir, K Bandara International Journal of Forecasting, 2019	1272	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	462	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	422	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	228	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	203	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	151	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	127*	2021
Global models for time series forecasting: A simulation study H Hewamalage, C Bergmeir, K Bandara Pattern Recognition, 108441, 2021	83	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	55	2021
Insights into the accuracy of social scientists’ forecasts of societal change Nature human behaviour 7 (4), 484-501, 2023	32	2023
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	30	2022
Commentary on the M5 forecasting competition S Kolassa International Journal of Forecasting 38 (4), 1562-1568, 2022	26*	2022
The Importance of Environmental Factors in Forecasting Australian Power Demand A Eshragh, B Ganim, T Perkins, K Bandara Environmental Modeling & Assessment, 2020	20	2020
Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data M Perera, J De Hoog, K Bandara, D Senanayake, S Halgamuge Applied Energy 361, 122971, 2024	19	2024
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	18	2020
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	16	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	15	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	13	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
Advanced genetic algorithm for optimal microgrid scheduling considering solar and load forecasting, battery degradation, and demand response dynamics WMN Witharama, K Bandara, MI Azeez, K Bandara, V Logeeshan, ... IEEE Access, 2024	10	2024

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