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 10 of 99
2023
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
Machine Learning85 citationsCross-domain graph anomaly detection via anomaly-aware contrastive alignment
Q Wang, G Pang, M Salehi, W Buntine, C Leckie
Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4676-4684, 2023
Machine Learning57 citations- Forecasting42 citations
Adaptive dependency learning graph neural networks
A Sriramulu, N Fourrier, C Bergmeir
Information Sciences 625, 700-714, 2023
Machine Learning33 citationsA survey on out-of-distribution evaluation of neural nlp models
X Li, M Liu, S Gao, W Buntine
arXiv preprint arXiv:2306.15261, 2023
Machine Learning31 citationsHandling 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
Forecasting30 citationsEliminating the impossible, whatever remains must be true: On extracting and applying background knowledge in the context of formal explanations
J Yu, A Ignatiev, PJ Stuckey, N Narodytska, J Marques-Silva
Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4123-4131, 2023
Machine Learning30 citationsAuc maximization for low-resource named entity recognition
ND Nguyen, W Tan, L Du, W Buntine, R Beare, C Chen
Proceedings of the AAAI Conference on Artificial Intelligence 37 (11), 13389 …, 2023
Research23 citationsLoMEF: 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
Forecasting22 citationsOn formal feature attribution and its approximation
J Yu, A Ignatiev, PJ Stuckey
arXiv preprint arXiv:2307.03380, 2023
Research21 citationsTracking progress in multi-agent path finding
B Shen, Z Chen, MA Cheema, DD Harabor, PJ Stuckey
arXiv preprint arXiv:2305.08446, 2023
Multi-Agent Systems19 citationsAnytime approximate formal feature attribution
J Yu, G Farr, A Ignatiev, PJ Stuckey
arXiv preprint arXiv:2312.06973, 2023
Research18 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.