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 92 of 99
1994
Computing second derivatives in feed-forward networks: A review
WL Buntine, AS Weigend
IEEE transactions on Neural Networks 5 (3), 480-488, 1994
Research186 citationsThe Aditi deductive database system
J Vaghanl, K Ramamohanarao, DB Kemp, Z Somogyi, PJ Stuckey, ...
The VLDB Journal 3 (2), 245-288, 1994
Research114 citationsBeyond finite domains
J Jaffar, MJ Maher, PJ Stuckey, RHC Yap
International Workshop on Principles and Practice of Constraint Programming …, 1994
Machine Learning113 citationsIncremental analysis of logic programs
MV Hermenegildo, K Marriott, PJ Stuckey
Technical University of Madrid (UPM), 1994
Research67 citationsA guide to the literature on learning graphical models
WL Buntine, P Friedland
Bayesian Methods59 citationsCompiling query constraints
PJ Stuckey, S Sudarshan
Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on …, 1994
Machine Learning41 citationsOn solving equations and disequations
WL Buntine, HJ Bürckert
Saarländische Universitäts-und Landesbibliothek, 1989
Research36 citationsApproximating interaction between linear arithmetic constraints
K Marriott, PJ Stuckey
Proceedings of the 1994 International Symposium on Logic programming, 571-585, 1994
Machine Learning32 citationsNeural networks and related methods for classification-discussion
P Whittle, J Kay, D Hand, L Tarassenko, P Brown, D Titterington, C Taylor, ...
Journal of the Royal Statistical Society Series B-Methodological 56 (3), 1994
Machine Learning14 citationsRepresenting learning with graphical models
WL Buntine, H Lum Jr
Bayesian Methods7 citations- Research1 citations
SPECIAL ISSUE ON EVALUATING AND CHANGING REPRESENTATION-GUEST EDITORIAL
K Morik, F Bergadano, W Buntine
Machine Learning 14 (2), 137-138, 1994
Research1 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.