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 60 of 99
2011
- Research43 citations
Dantzig-Wolfe decomposition and branch-and-price solving in G12
J Puchinger, PJ Stuckey, MG Wallace, S Brand
Constraints 16 (1), 77-99, 2011
Research36 citationsOptimal carpet cutting
A Schutt, PJ Stuckey, AR Verden
International Conference on Principles and Practice of Constraint …, 2011
Research26 citationsBoolean equi-propagation for optimized SAT encoding
A Metodi, M Codish, V Lagoon, PJ Stuckey
International Conference on Principles and Practice of Constraint …, 2011
Research20 citationsForecaster performance evaluation with cross-validation and variants
C Bergmeir, JM Benitez
2011 11th International Conference on Intelligent Systems Design and …, 2011
Forecasting19 citationsCP and IP approaches to cancer radiotherapy delivery optimization
D Baatar, N Boland, S Brand, PJ Stuckey
Constraints 16 (2), 173-194, 2011
Optimization15 citationsPiecewise linear approximation of protein structures using the principle of minimum message length
AS Konagurthu, L Allison, PJ Stuckey, AM Lesk
Bioinformatics 27 (13), i43-i51, 2011
Research14 citationsReducing chaos in sat-like search: Finding solutions close to a given one
I Abío, M Deters, R Nieuwenhuis, PJ Stuckey
International conference on theory and applications of satisfiability …, 2011
Optimization11 citationsOptimal automatic table layout
G Gange, K Marriott, P Moulder, P Stuckey
Proceedings of the 11th ACM symposium on Document engineering, 23-32, 2011
Research6 citationsConstraints in non-boolean contexts
L De Koninck, S Brand, PJ Stuckey
Technical Communications of the 27th International Conference on Logic …, 2011
Machine Learning6 citationsMemoizing a monadic mixin DSL
P Wuille, T Schrijvers, H Samulowitz, G Tack, P Stuckey
International Workshop on Functional and Constraint Logic Programming, 68-85, 2011
Research2 citationsExplaining ßow-based propagation
N Downing, T Feydy, PJ Stuckey
Machine Learning
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.