Research & Decision Science
Where forecasting research becomes operational decision infrastructure
RabbitHawk is built on peer-reviewed work in probabilistic forecasting, hierarchical reconciliation, optimization and applied AI, translated into systems that improve real decisions under real constraints.
- publications
- 1,181publications
- citations
- 53,485citations
- highly cited papers
- Top 1%highly cited papers
Scientific foundations
Methods proven in the literature, applied in production
Our team’s research is recognized at the field’s leading venues and used at scale by major organizations. We bring that rigor to enterprise planning, but judge every method by whether it improves decisions in practical settings.
Research → product
| Research area | Enterprise problem | RabbitHawk capability |
|---|---|---|
| Hierarchical forecasting | Local plans do not add up to executive targets | Reconciled forecasts across every level |
| Probabilistic forecasting | Point forecasts hide risk | Full predictive distributions, intervals and scenario ranges |
| Optimization | Forecasts do not tell teams what to do | Constraint-aware recommendations |
| Agentic AI | Context is trapped in emails and notes | Context ingestion and decision agents |
| Human-in-the-loop AI | Automation without trust creates resistance | Approval, override and audit trails |
| Alignment science | Teams optimize against conflicting goals | 90North alignment and goal clarity |
Hierarchical forecasting
Local plans do not add up to executive targets
Reconciled forecasts across every level
Probabilistic forecasting
Point forecasts hide risk
Full predictive distributions, intervals and scenario ranges
Optimization
Forecasts do not tell teams what to do
Constraint-aware recommendations
Agentic AI
Context is trapped in emails and notes
Context ingestion and decision agents
Human-in-the-loop AI
Automation without trust creates resistance
Approval, override and audit trails
Alignment science
Teams optimize against conflicting goals
90North alignment and goal clarity
Selected publications
The full peer-reviewed database: search by title, author, venue, category or year.
1,181 results · page 19 of 99
2022
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language …
Y Hanqi, Y Zonghan, S Ruder, W Xiaojun
Research2022 Index IEEE Computational Intelligence Magazine Vol. 17
H Abbass, JM Alonso-Moral, J Andreu-Perez, M Bennamoun, ...
IEEE Computational Intelligence Magazine 17 (4), 2022
ResearchHardness-guided domain adaptation to recognize biomedical named entities under low-resource scenarios
W Buntine, C Chen, R Beare, L Du
Machine Learning
2021
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
Machine Learning1,740 citationsMonash time series forecasting archive
R Godahewa, C Bergmeir, GI Webb, RJ Hyndman, P Montero-Manso
arXiv preprint arXiv:2105.06643, 2021
Forecasting328 citationsMultiRocket: multiple pooling operators and transformations for fast and effective time series classification
CW Tan, A Dempster, C Bergmeir, GI Webb
arXiv preprint arXiv:2102.00457, 2021
Forecasting252 citationsIntegrated task assignment and path planning for capacitated multi-agent pickup and delivery
Z Chen, J Alonso-Mora, X Bai, DD Harabor, PJ Stuckey
IEEE Robotics and Automation Letters 6 (3), 5816-5823, 2021
Optimization244 citationsNeuralprophet: Explainable forecasting at scale
O Triebe, H Hewamalage, P Pilyugina, N Laptev, C Bergmeir, ...
arXiv preprint arXiv:2111.15397, 2021
Machine Learning230 citationsTopic modelling meets deep neural networks: A survey
H Zhao, D Phung, V Huynh, Y Jin, L Du, W Buntine
arXiv preprint arXiv:2103.00498, 2021
Machine Learning203 citationsImproving 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
Forecasting200 citationsTime 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
Forecasting181 citationsAnytime multi-agent path finding via large neighborhood search
J Li, Z Chen, D Harabor, PJ Stuckey, S Koenig
International Joint Conference on Artificial Intelligence 2021, 4127-4135, 2021
Optimization151 citations
Learn the science behind the engine
“Forecasting for Data Scientists”, a free 30-chapter video course by co-founder Dr Christoph Bergmeir, from fundamentals to advanced deep learning.