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 65 of 99
2009
Machine learning and knowledge discovery in databases
L AlSumait, D Barbará, J Gentle, C Domeniconi, W Buntine, M Grobelnik, ...
Springer, 2009
Machine Learning6 citationsOperator guidance in 2D echocardiography via 3D model to image registration
C Bergmeir, N Subramanian
Medical Imaging 2009: Ultrasonic Imaging and Signal Processing 7265, 385-390, 2009
Research2 citationsPropagating systems of dense linear integer constraints
T Feydy, PJ Stuckey
Constraints 14 (2), 235-253, 2009
Machine Learning1 citationsKlassifikation von Standardebenen in der 2D-Echokardiographie mittels 2D-3D-Bildregistrierung
C Bergmeir, N Subramanian
Bildverarbeitung für die Medizin 2009: Algorithmen—Systeme—Anwendungen …, 2009
Researchwww. website4free. co. nz
R Alidarso, Z Anguelov, T Bahlen, F De Nijs
ResearchDemand-driven Normalisation for ACD Term Rewriting
L De Koninck, GJ Duck, PJ Stuckey
International Conference on Logic Programming, 484-488, 2009
ResearchErratum to: Efficient constraint propagation engines
C Schulte, PJ Stuckey
ACM Transactions on Programming Languages and Systems (TOPLAS) 31 (2), 2009
Machine LearningBranch-and-Price Solving in G12
J Puchinger, P Stuckey, M Wallace, S Brand
Schloss Dagstuhl–Leibniz-Zentrum für Informatik, 2009
ResearchExploring scale-induced feature hierarchies in natural images
J Perkiö, T Tuytelaars, W Buntine
2009 International Conference on Machine Learning and Applications, 25-31, 2009
ResearchGuest editors’ introduction: special issue of selected papers from ECML PKDD 2009.
A Kolcz, D Mladenic, W Buntine, M Grobelnik, J Shawe-Taylor
ResearchProceedings of the 2009th European Conference on Machine Learning and Knowledge Discovery in Databases-Volume Part II
W Buntine, M Grobelnik, D Mladenić, J Shawe-Taylor
Springer-Verlag, 2009
Machine LearningGuest editors’ introduction: Special Issue from ECML PKDD 2009
A Kołcz, D Mladenić, W Buntine, M Grobelnik, J Shawe-Taylor
Research
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.