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 70 of 99
2007
Practical preference relations for large data sets
KA Ross, PJ Stuckey, A Marian
2007 IEEE 23rd International Conference on Data Engineering Workshop, 229-236, 2007
Research5 citationsConstraint propagation for loose constraint graphs
K Francis, PJ Stuckey
Proceedings of the 2007 ACM symposium on Applied computing, 334-335, 2007
Machine Learning2 citationsPropagating dense systems of integer linear equations
T Feydy, PJ Stuckey
Proceedings of the 2007 ACM symposium on Applied computing, 306-310, 2007
Research2 citations- Research
2006
MUSTANG: a multiple structural alignment algorithm
AS Konagurthu, JC Whisstock, PJ Stuckey, AM Lesk
Proteins: Structure, Function, and Bioinformatics 64 (3), 559-574, 2006
Research870 citationsFinite domain bounds consistency revisited
CW Choi, W Harvey, JHM Lee, PJ Stuckey
Australasian Joint Conference on Artificial Intelligence, 49-58, 2006
Machine Learning95 citationsSolving partial order constraints for LPO termination
M Codish, V Lagoon, PJ Stuckey
International Conference on Rewriting Techniques and Applications, 4-18, 2006
Machine Learning53 citationsAutomatic implication checking for CHR constraints
T Schrijvers, B Demoen, G Duck, P Stuckey, T Frühwirth
Electronic notes in theoretical computer science 147 (1), 93-111, 2006
Machine Learning42 citationsA framework for extended algebraic data types
M Sulzmann, J Wazny, PJ Stuckey
International Symposium on Functional and Logic Programming, 47-64, 2006
Machine Learning41 citationsACD term rewriting
GJ Duck, PJ Stuckey, S Brand
International Conference on Logic Programming, 117-131, 2006
Research38 citationsTag-based navigation for peer-to-peer wikipedia
J Fokker, J Pouwelse, W Buntine
Collaborative Web Tagging Workshop at WWW2006, Edinburgh, 2006
Research35 citationsConstraint logic programming
K Marriott, PJ Stuckey, M Wallace
Foundations of Artificial Intelligence 2, 409-452, 2006
Machine Learning31 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.