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 24 of 99
2021
Lightweight nontermination inference with CHCs
B Kafle, G Gange, P Schachte, H Søndergaard, PJ Stuckey
International Conference on Software Engineering and Formal Methods, 383-402, 2021
Bayesian Methods1 citationsMulti-Target Search in Euclidean Space with Ray Shooting
R Hechenberger, DD Harabor, MA Cheema, PJ Stuckey, P Le Bodic
Proceedings of the International Symposium on Combinatorial Search 12 (1 …, 2021
Optimization1 citationsEvaluating Meta-Reinforcement Learning through a HVAC Control Benchmark (Student Abstract)
YS Grewal, F de Nijs, S Goodwin
Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15785 …, 2021
Machine LearningML4CO submission EFPP
E Lam, F de Nijs, P Le Bodic, P Stuckey
Advances in Neural Information Processing Systems Competition 2021: Machine …, 2021
ResearchPlanning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021
B Say, S Sanner, J Devriendt, J Nordström, PJ Stuckey
arXiv preprint arXiv:2108.00633, 2021
Machine LearningIntegration of Constraint Programming, Artificial Intelligence, and Operations Research
PJ Stuckey
Springer International Publishing, 2021
Machine LearningThe Neglected Sibling: Isotropic Gaussian Posterior for VAE
L Zhang, W Buntine, E Shareghi
arXiv preprint arXiv:2110.07383, 2021
ResearchTemporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
B Hettige, W Wang, YF Li, S Le, W Buntine
arXiv preprint arXiv:2102.02586, 2021
Forecasting
2020
Learning classification trees
W Buntine
Artificial Intelligence frontiers in statistics, 182-201, 2020
Research693 citationsForecasting 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
Machine Learning591 citationsReinforcement learning for whole-building HVAC control and demand response
D Azuatalam, WL Lee, F de Nijs, A Liebman
Energy and AI 2, 100020, 2020
Machine Learning284 citationsLSTM-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
Forecasting248 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.