Kasun Bandara
Kasun Bandara
Forecast Scientist, EnergyAustralia | Honorary Research Fellow, University of Melbourne
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Cited by
Cited by
Recurrent neural networks for time series forecasting: Current status and future directions
H Hewamalage, C Bergmeir, K Bandara
International Journal of Forecasting, 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
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
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
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
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
Global models for time series forecasting: A simulation study
H Hewamalage, C Bergmeir, K Bandara
Pattern Recognition, 108441, 2021
MSTL: a seasonal-trend decomposition algorithm for time series with multiple seasonal patterns
K Bandara, RJ Hyndman, C Bergmeir
arXiv preprint arXiv:2107.13462, 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
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
The Importance of Environmental Factors in Forecasting Australian Power Demand
A Eshragh, B Ganim, T Perkins, K Bandara
Environmental Modeling & Assessment, 2020
Commentary on the M5 forecasting competition
S Kolassa
International Journal of Forecasting 38 (4), 1562-1568, 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
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
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
Insights into the accuracy of social scientists’ forecasts of societal change
Nature Human Behaviour 7 (4), 484-501, 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
Towards optimising Wi-Fi energy consumption in mobile phones: A data driven approach
K Bandara, A Caldera
Proceedings of the 2015 Advances in ICT for Emerging Regions, 226-235, 2015
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
An Interpretable Machine Learning Approach to Predicting Customer Behavior on JD. Com
F Iravani, S Alizamir, A Eshragh, K Bandara
Com (October 1, 2020), 2020
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