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 80 of 99
2003
- Research8 citations
The usefulness of constraints for diagram editing
M Wybrow, K Marriott, L McIver, PJ Stuckey
Proceedings of the 2003 Australasian Computer Human Interaction Conference …, 2003
Machine Learning4 citationsAutomatic derivation of the multinomial PCA algorithm
W Buntine, B Fischer, AG Gray
Technical report, NASA/Ames, 2003. Available at http://ase. arc. nasa. gov …, 2003
Research4 citationsLanguage models for intelligent search using multinomial PCA
W Buntine, P Myllymäki, S Perttu
Proceedings of the First European Web Mining Forum at the 14th European …, 2003
Optimization3 citationsThe Chameleon type debugger (tool demonstration)
PJ Stuckey, M Sulzmann, J Wazny
arXiv preprint cs/0311023, 2003
Research2 citations- Machine Learning2 citations
Logic Programming: 18th International Conference, ICLP 2002, Copenhagen, Denmark, July 29-August 1, 2002 Proceedings
PJ Stuckey
Springer, 2003
Research2 citationsEfficient computing of stochastic complexity
P Kontkanen, WL Buntine, P Myllymäki, J Rissanen, H Tirri
International Workshop on Artificial Intelligence and Statistics, 171-178, 2003
Research2 citationsImproving nogood recording using 2SAT
PJ Stuckey, L Zheng
Proceedings. 15th IEEE International Conference on Tools with Artificial …, 2003
Research1 citationsType debugging in the hindley/milner system with overloading
PJ Stuckey, M Sulzmann, J Wazny
Research1 citationsMultiCPL’03: Second International Workshop on Multiparadigm Constraint Programming Languages
P Hofstedt, M Hanus, A Wolf, S Abdennadher, T Frühwirth, M Grabmüller, ...
Machine Learning
2002
A guide to the literature on learning probabilistic networks from data
W Buntine
IEEE Transactions on knowledge and data engineering 8 (2), 195-210, 2002
Bayesian Methods770 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.