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 95 of 99
1992
Introduction to IND version 2.1 and Recursive Partitioning
W Buntine, R Caruana
NASA Ames Research Center, Moffett Field, CA, 1992
Research58 citationsQuery restricted bottom-up evaluation of normal logic programs.
DB Kemp, PJ Stuckey, D Srivastava
JICSLP, 288-302, 1992
Research34 citationsTransforming normal logic programs to constraint logic programs
K Kanchanasut, PJ Stuckey
Theoretical computer science 105 (1), 27-56, 1992
Machine Learning11 citationsAditi-Prolog language manual
J Harland, DB Kemp, TS Leask, K Ramamohanarao, JA Shepherd, ...
Deductive Database Group, The University of Melbourne, 1992
Research5 citationsAditi users' guide
J Harland, DB Kemp, TS Leask, K Ramamohanarao, JA Shepherd, ...
Deductive Database Group, The University of Melbourne, 1992
Research5 citationsLearning classification
W Buntine
Artificial Intelligence Frontiers in Statistics: Al and Statistics III 3, 182, 1992
Research- Research
1991
- Bayesian Methods1,238 citations
- Bayesian Methods752 citations
Semantics of logic programs with aggregates
DB Kemp, PJ Stuckey
International Logic Programming Symposium 1991, 1991
Research165 citationsConstructive negation for constraint logic programming
PJ Stuckey
Proceedings 1991 Sixth Annual IEEE Symposium on Logic in Computer Science …, 1991
Machine Learning157 citationsMagic sets and bottom-up evaluation of well-founded models.
DB Kemp, D Srivastava, PJ Stuckey
ISLP, 337-351, 1991
Research80 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.