Hansika Hewamalage
Hansika Hewamalage
Data Scientist, Coles Group
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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, 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
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
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
Neuralprophet: Explainable forecasting at scale
O Triebe, H Hewamalage, P Pilyugina, N Laptev, C Bergmeir, ...
arXiv preprint arXiv:2111.15397, 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
Global models for time series forecasting: A simulation study
H Hewamalage, C Bergmeir, K Bandara
Pattern Recognition 124, 108441, 2022
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
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
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
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
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
Advancing Time Series Forecasting Techniques & Practices in a Big Data Environment
Monash University, 2022
Global models for time series forecasting: A simulation study
H Hewamalage, C Bergmeir, K Bandara
40th International Symposium on Forecasting, 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
Forecasting Hierarchical Time Series Using Non-Linear Mappings
S Wickramasuriya, K Bandara, H Hewamalage, M Perera
Available at SSRN 4793559, 0
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