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Kasun Bandara
Kasun Bandara
Forecast Scientist, EnergyAustralia | Honorary Research Fellow, University of Melbourne
Verified email at unimelb.edu.au - Homepage
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
7992019
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
371*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
3302019
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
1802019
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
1522020
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
1072021
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
652021
Global models for time series forecasting: A simulation study
H Hewamalage, C Bergmeir, K Bandara
Pattern Recognition, 108441, 2021
522021
Ensembles of localised models for time series forecasting
R Godahewa, K Bandara, GI Webb, S Smyl, C Bergmeir
Knowledge-Based Systems 233, 107518, 2021
292021
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
182020
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
162022
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
152020
Insights into the accuracy of social scientists’ forecasts of societal change
Nature human behaviour 7 (4), 484-501, 2023
132023
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
102022
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
10
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
72022
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
52023
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
42021
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
32021
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Articles 1–20