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).
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
3

Package ‘frbs’

LS Riza, C Bergmeir, F Herrera, JM Benitez
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)