Research
& publications
We’re not academic for the sake of it.
Our software, its algorithms and processes, are all at the forefront of global knowledge—reinforced by real-world behaviour and analysis.
Put simply: you cannot innovate at the leading edge without a team who can think beyond the leading edge.
Below are some relevant publications from our team—over 150 research articles, with over 9,200 peer citations—in collaboration with scientists from around the world who are leaders in their own domains. Many of the publications on forecasting have been Clarivate Web of Science Highly Cited Papers (top 1% of their research field).
Below are some relevant publications from our team—over 150 research articles, with over 9,200 peer citations—in collaboration with scientists from around the world who are leaders in their own domains. Many of the publications on forecasting have been Clarivate Web of Science Highly Cited Papers (top 1% of their research field).
2024
Cited by
2
Local and global trend Bayesian exponential smoothing models
S Smyl, C Bergmeir, A Dokumentov, X Long, E Wibowo, D Schmidt
International Journal of Forecasting, 2024
2024
Cited by
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
X Zhou, W Wang, W Buntine, S Qu, A Sriramulu, W Tan, C Bergmeir
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
2024
Cited by
Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model
X Long, DF Schmidt, C Bergmeir, S Smyl
arXiv preprint arXiv:2407.00492, 2024
2024
Cited by
Counterfactual Predictions in Shared Markets: A Global Forecasting Approach with Deep Learning and Spillover Considerations
P Grecov, K Ackermann, C Bergmeir
SSRN
2024
Cited by
Deep Active Audio Feature Learning in Resource-Constrained Environments
M Mohaimenuzzaman, C Bergmeir, B Meyer
IEEE/ACM Transactions on Audio, Speech, and Language Processing
2024
Cited by
How Well Can Social Scientists Forecast Societal Change?
I Grossmann, C Bergmeir, P Slattery
Foresight: The International Journal of Applied Forecasting
2024
Cited by
Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems
Genov E, Ruddick J, Bergmeir C, Vafaeipour M, Coosemans T, Garcia S, Messagie M.
arXiv preprint arXiv:2407.03368, 2024
2024
Cited by
Commentary: Can LLMs Provide Good Forecasts?
C Bergmeir
Foresight: The International Journal of Applied Forecasting, 18-20
2024
Cited by
LLMs and Foundational Models: Not (Yet) as Good as Hoped.
C Bergmeir
Foresight: The International Journal of Applied Forecasting 73
2024
Cited by
DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
A Sriramulu, N Fourrier, C Bergmeir
arXiv preprint arXiv:2405.18693, 2024
2024
Cited by
Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
A Sriramulu, C Bergmeir, S Smyl
arXiv preprint arXiv:2405.07117
2024
Cited by
Causal Effect Estimation With Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand
A Nandipura Prasanna, P Grecov, A Dieyu Weng, CN Bergmeir
IEEE
2024
Cited by
Bridging High-Voltage and Medium-Voltage Electricity Demand Forecasting
Triebe O, Wittner S, Wagner L, Arend J, Sun T, Zanocco C, Miltner M, Ghesmati A, Tsai CH, Bergmeir C, Rajagopal R
SSRN 4953113
2024
Cited by
Graph Neural Network based forecasting framework for large scale multivariate time series
A SRIRAMULU
2023
Cited by
96
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
Cited by
42
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
M Mohaimenuzzaman, C Bergmeir, I West, B Meyer
Pattern Recognition 133, 109025
2023
Cited by
40
An overview of clustering methods with guidelines for application in mental health research
Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, Bayer J, Menssink JM, Wang T, Bergmeir C, Wood S.
Psychiatry Research 327, 115265
2023
Cited by
20
Adaptive dependency learning graph neural networks
A Sriramulu, N Fourrier, C Bergmeir
Information Sciences 625, 700-714
2023
Cited by
19
Insights into the accuracy of social scientists’ forecasts of societal change
C Bergmeir
Nature human behaviour 7 (4), 484-501
2023
Cited by
13
LoMEF: A framework to produce local explanations for global model time series forecasts
D Rajapaksha, C Bergmeir, RJ Hyndman
International Journal of Forecasting 39 (3), 1424-1447
2023
Cited by
8
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
Cited by
7
An accurate and fully-automated ensemble model for weekly time series forecasting
R Godahewa, C Bergmeir, GI Webb, P Montero-Manso
International Journal of Forecasting 39 (2), 641-658
2023
Cited by
4
Time series adversarial attacks: an investigation of smooth perturbations and defense approaches
Pialla G, Ismail Fawaz H, Devanne M, Weber J, Idoumghar L, Muller PA, Bergmeir C, Schmidt DF, Webb GI, Forestier G
International Journal of Data Science and Analytics
2023
Cited by
3
SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting
R Godahewa, GI Webb, D Schmidt, C Bergmeir
Machine Learning 112 (7), 2555-2591
2023
Cited by
2
On Forecast Stability
R Godahewa, C Bergmeir, ZE Baz, C Zhu, Z Song, S García, D Benavides
arXiv preprint arXiv:2310.17332,
2023
Cited by
2
Tree-based survival analysis improves mortality prediction in cardiac surgery
Penny-Dimri JC, Bergmeir C, Reid CM, Williams-Spence J, Perry LA, Smith JA
Frontiers in Cardiovascular Medicine 10, 1211600
2023
Cited by
2
Common Pitfalls and Better Practices in Forecast Evaluation for Data Scientists.
C Bergmeir
Foresight: The International Journal of Applied Forecasting
2023
Cited by
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach
Long X, Bui Q, Oktavian G, Schmidt DF, Bergmeir C, Godahewa R, Lee SP, Zhao K, Condylis P
arXiv preprint arXiv:2311.00993
2023
Cited by
Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network
Penny-Dimri JC, Bergmeir C, Reid CM, Williams-Spence J, Cochrane AD, Smith JA
Plos one 18 (8), e0289930
2023
Cited by
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
Pekaslan D, Alonso-Moral JM, Bandara K, Bergmeir C, Bernabe-Moreno J, Eigenmann R, Einecke N, Ergen S, Godahewa R, Hewamalage H, Lago J
arXiv preprint arXiv:2311.04007
2023
Cited by
Package ‘Rmalschains’
C Bergmeir, JM Benítez, D Molina, R Davies, D Eddelbuettel, N Hansen
2023
Cited by
Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand
AN Prasanna, P Grecov, AD Weng, C Bergmeir
IEEE Transactions on Power Systems
2022
Cited by
768
Forecasting: theory and practice
Petropoulos F, Apiletti D, Assimakopoulos V, Babai MZ, Barrow DK, Taieb SB, Bergmeir C, Bessa RJ, Bijak J, Boylan JE, Browell J
International Journal of Forecasting
2022
Cited by
144
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
CW Tan, A Dempster, C Bergmeir, GI Webb
Data Mining and Knowledge Discovery 36 (5), 1623-1646
2022
Cited by
68
Global models for time series forecasting: A simulation study
H Hewamalage, C Bergmeir, K Bandara
Pattern Recognition 124, 108441
2022
Cited by
21
Pruning vs XNOR-Net: A comprehensive study of deep learning for audio classification on edge-devices
M Mohaimenuzzaman, C Bergmeir, B Meyer
IEEE Access 10, 6696-6707
2022
Cited by
20
Model selection in reconciling hierarchical time series
M Abolghasemi, RJ Hyndman, E Spiliotis, C Bergmeir
Machine Learning, 1-51
2022
Cited by
18
Association between urine output and mortality in critically ill patients: a machine learning approach
Heffernan AJ, Judge S, Petrie SM, Godahewa R, Bergmeir C, Pilcher D, Nanayakkara S
Critical care medicine
2022
Cited by
17
Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions
M Abolghasemi, G Tarr, C Bergmeir
International Journal of Forecasting
2022
Cited by
16
Limref: Local interpretable model agnostic rule-based explanations for forecasting, with an application to electricity smart meter data
D Rajapaksha, C Bergmeir
Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 12098
2022
Cited by
13
Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta‐analysis
JC Penny‐Dimri, C Bergmeir, L Perry, L Hayes, R Bellomo, JA Smith
Journal of Cardiac Surgery 37 (11), 3838-3845
2022
Cited by
13
Smooth perturbations for time series adversarial attacks
Pialla G, Fawaz HI, Devanne M, Weber J, Idoumghar L, Muller PA, Bergmeir C, Schmidt D, Webb G, Forestier G
Pacific-Asia Conference on Knowledge Discovery and Data Mining
2022
Cited by
9
A generative deep learning framework across time series to optimize the energy consumption of air conditioning systems
R Godahewa, C Deng, A Prouzeau, C Bergmeir
IEEE Access 10, 6842-6855
2022
Cited by
9
Probabilistic causal effect estimation with global neural network forecasting models
Grecov P, Prasanna AN, Ackermann K, Campbell S, Scott D, Lubman DI, Bergmeir C
IEEE Transactions on Neural Networks and Learning Systems
2022
Cited by
6
Comparison and evaluation of methods for a predict+ optimize problem in renewable energy
Bergmeir C, de Nijs F, Sriramulu A, Abolghasemi M, Bean R, Betts J, Bui Q, Dinh NT, Einecke N, Esmaeilbeigi R, Ferraro S
arXiv preprint arXiv:2212.10723
2022
Cited by
2
FRANS: Automatic feature extraction for time series forecasting
A Chernikov, CW Tan, P Montero-Manso, C Bergmeir
arXiv preprint arXiv:2209.07018
2022
Cited by
Dealing with missing data using attention and latent space regularization
JC Penny-Dimri, C Bergmeir, J Smith
arXiv preprint arXiv:2211.07059
2022
Cited by
Evaluating individual heterogeneity in mental health research: an overview of clustering methods and guidelines for applications
Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, Bayer JM, Menssink J, Wang T, Bergmeir C, Wood S.
PsyArXiv
2022
Cited by
RNN-BOF: A Multivariate Global Recurrent Neural Network for Binary Outcome Forecasting of Inpatient Aggression
A Quinn, M Simmons, B Spivak, C Bergmeir
2022 International Joint Conference on Neural Networks (IJCNN), 1-8
2022
Cited by
Transient Stability Assessment Using Modular Deep Nets For Power Network Topology Changes
S Meghdadi, G Tack, A Liebman, N Langrené, C Bergmeir
2022
Cited by
Designing Less Forgetful Networks for Continual Learning
Nicholas I, Kuo H, Harandi M, Fourrier N, Ferraro G, Walder C, Suominen H
2021
Cited by
1049
Recurrent neural networks for time series forecasting: Current status and future directions
H Hewamalage, C Bergmeir, K Bandara
International Journal of Forecasting 37 (1), 388-427
2021
Cited by
151
Monash time series forecasting archive
R Godahewa, C Bergmeir, GI Webb, RJ Hyndman, P Montero-Manso
arXiv preprint arXiv:2105.06643
2021
Cited by
150
Neuralprophet: Explainable forecasting at scale
Triebe O, Hewamalage H, Pilyugina P, Laptev N, Bergmeir C
arXiv preprint arXiv:2111.15397
2021
Cited by
132
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
Cited by
108
Time series extrinsic regression: Predicting numeric values from time series data
CW Tan, C Bergmeir, F Petitjean, GI Webb
Data Mining and Knowledge Discovery 35 (3), 1032-1060
2021
Cited by
94
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
Cited by
45
SQAPlanner: Generating data-informed software quality improvement plans
Rajapaksha D, Tantithamthavorn C, Jiarpakdee J, Bergmeir C, Grundy J, Buntine W
IEEE Transactions on Software Engineering
2021
Cited by
43
Ensembles of localised models for time series forecasting
R Godahewa, K Bandara, GI Webb, S Smyl, C Bergmeir
Knowledge-Based Systems 233, 107518
2021
Cited by
42
Machine learning algorithms for predicting and risk profiling of cardiac surgery-associated acute kidney injury
Penny-Dimri JC, Bergmeir C, Reid CM, Williams-Spence J, Cochrane AD, Smith JA
Seminars in thoracic and cardiovascular surgery 33 (3), 735-745
2021
Cited by
15
MultiRocket: Effective summary statistics for convolutional outputs in time series classification
CW Tan, A Dempster, C Bergmeir, GI Webb
arXiv preprint arXiv:2102.00457
2021
Cited by
10
Learning to continually learn rapidly from few and noisy data
Nicholas I, Kuo H, Harandi M, Fourrier N, Walder C, Ferraro G, Suominen H
AAAI Workshop on Meta-Learning and MetaDL Challenge
2021
Cited by
8
I Know What You Know: What Hand Movements Reveal about Domain Expertise
S Oviatt, J Lin, A Sriramulu
ACM Transactions on Interactive Intelligent Systems 11 (1), 1-26
2021
Cited by
8
forecast: Forecasting functions for time series and linear models (Version 8.14)
Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F, Ihaka R
2021
Cited by
8
Causal inference using global forecasting models for counterfactual prediction
Grecov P, Bandara K, Bergmeir C, Ackermann K, Campbell S, Scott D, Lubman D
Pacific-Asia Conference on Knowledge Discovery and Data Mining
2021
Cited by
7
Plastic and stable gated classifiers for continual learning
NI Kuo, M Harandi, N Fourrier, C Walder, G Ferraro, H Suominen
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3553-3558)
2021
Cited by
5
Versatile and robust transient stability assessment via instance transfer learning
S Meghdadi, G Tack, A Liebman, N Langrené, C Bergmeir
IEEE Power & Energy Society General Meeting (PESGM), 1-5
2021
Cited by
5
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
Cited by
3
ssc: An R Package for Semi-Supervised Classification
M González, O Rosado, JD Rodríguez, C Bergmeir, I Triguero, JM Benítez
R package version 21-0
2021
Cited by
1
Dependency Learning Graph Neural Network for Multivariate Forecasting
A Patel, A Sriramulu, C Bergmeir, N Fourrier
Neural Information Processing: 28th International Conference
2021
Cited by
Learning to Continually Learn Rapidly from Few and Noisy Data
M Harandi, N Fourrier, C Walder, G Ferraro, H Suominen
arXiv e-prints, arXiv: 2103.04066
2021
Cited by
Highway-Connection Classifier Networks for Plastic yet Stable Continual Learning
Nicholas I, Kuo H, Harandi M, Fourrier N, Walder C, Ferraro G, Suominen H
2020
Cited by
380
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
2020
Cited by
180
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
Cited by
178
Package forecast: Forecasting functions for time series and linear models
Hyndman RJ, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E
https://cran.r-project.org/web/packages/forecast/forecast.pdf
2020
Cited by
38
Monash University, UEA, UCR time series extrinsic regression archive
CW Tan, C Bergmeir, F Petitjean, GI Webb
arXiv preprint arXiv:2006.10996
2020
Cited by
36
LoRMIkA: Local rule-based model interpretability with k-optimal associations
D Rajapaksha, C Bergmeir, W Buntine
Information Sciences 540, 221-241
2020
Cited by
18
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
2020 International Joint Conference on Neural Networks (IJCNN), 1-10
2020
Cited by
13
An unsupervised data-driven model to classify gait patterns in children with cerebral palsy
Choisne J, Fourrier N, Handsfield G, Signal N, Taylor D, Wilson N, Stott S, Besier TF
Journal of clinical medicine 9 (5), 1432
2020
Cited by
11
Simulation and optimisation of air conditioning systems using machine learning
R Godahewa, C Deng, A Prouzeau, C Bergmeir
arXiv preprint arXiv:2006.15296
2020
Cited by
9
A comparison of characteristics and outcomes of patients admitted to the ICU with asthma in Australia and New Zealand and United states
Abdelkarim H, Durie M, Bellomo R, Bergmeir C, Badawi O, El-Khawas K, Pilcher D
Journal of Asthma
2020
Cited by
8
A strong baseline for weekly time series forecasting
R Godahewa, C Bergmeir, GI Webb, P Montero-Manso
arXiv preprint arXiv:2010.08158
2020
Cited by
8
Time series regression
CW Tan, C Bergmeir, F Petitjean, GI Webb
arXiv preprint arXiv:2006.12672
2020
Cited by
6
An input residual connection for simplifying gated recurrent neural networks
NIH Kuo, M Harandi, N Fourrier, C Walder, G Ferraro, H Suominen
2020 International Joint Conference on Neural Networks (IJCNN), 1-8
2020
Cited by
6
M2SGD: Learning to Learn Important Weights
NI Kuo, M Harandi, N Fourrier, C Walder, G Ferraro, H Suominen
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
2020
Cited by
3
Seasonal averaged one-dependence estimators: a novel algorithm to address seasonal concept drift in high-dimensional stream classification
R Godahewa, T Yann, C Bergmeir, F Petitjean
2020 International Joint Conference on Neural Networks (IJCNN), 1-8
2020
Cited by
2
MTL2L: A Context Aware Neural Optimiser
NI Kuo, M Harandi, N Fourrier, C Walder, G Ferraro, H Suominen
CoRR
2020
Cited by
Self-organising Neural Network Hierarchy
Borgohain S, Kowadlo G, Rawlinson D, Bergmeir C, Loo K, Rangarajan H, Kuhlmann L
Australasian Joint Conference on Artificial Intelligence, 359-370, 2020
2020
Cited by
A data-driven model to classify gait pattern in children with cerebral palsy
Choisne J, Fourrier N, Handsfield G, Signal N, Taylor D, Wilson N, Stott N, Besier T
MDPI AG
2019
Cited by
211
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
2019
Cited by
32
Machine learning applications in time series hierarchical forecasting
M Abolghasemi, RJ Hyndman, G Tarr, C Bergmeir
arXiv preprint arXiv:1912.00370
2019
Cited by
17
Closing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning
Baggio D, Peel T, Peleg AY, Avery S, Prayaga M, Foo M, Haffari G, Liu M, Bergmeir C, Ananda-Rajah M
Journal of Clinical Medicine 8, no. 9
2019
Cited by
10
Dynamic Adaptive Gesturing Predicts Domain Expertise in Mathematics
A Sriramulu, J Lin, S Oviatt
International Conference on Multimodal Interaction
2019
Cited by
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification
Nicholas I, Kuo H, Harandi MT, Fourrier N, Ferraro G, Walder C, Suominen H
2018
Cited by
690
A note on the validity of cross-validation for evaluating autoregressive time series prediction
C Bergmeir, RJ Hyndman, B Koo
Computational Statistics & Data Analysis 120, 70-83
2018
Cited by
172
Exploring the sources of uncertainty: Why does bagging for time series forecasting work?
F Petropoulos, RJ Hyndman, C Bergmeir
European Journal of Operational Research 268 (2), 545-554
2018
Cited by
124
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study
Nanayakkara S, Fogarty S, Tremeer M, Ross K, Richards B, Bergmeir C, Xu S, Stub D, Smith K, Tacey M, Liew D
PLoS medicine 15 (11), e1002709
2018
Cited by
32
Self-labeling techniques for semi-supervised time series classification: an empirical study
M González, C Bergmeir, I Triguero, Y Rodríguez, JM Benítez
Knowledge and Information Systems 55, 493-528
2018
Cited by
31
Multiobjective optimization for railway maintenance plans
Peralta D, Bergmeir C, Krone M, Galende M, Menéndez M, Sainz-Palmero GI, Martinez Bertrand C, Klawonn F, Benitez JM
Journal of Computing in Civil Engineering 32 (3)