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 66 of 99
2009
2008
The design of the Zinc modelling language
K Marriott, N Nethercote, R Rafeh, PJ Stuckey, M Garcia De La Banda, ...
Constraints 13 (3), 229-267, 2008
Research194 citationsEfficient constraint propagation engines
C Schulte, PJ Stuckey
ACM Transactions on Programming Languages and Systems (TOPLAS) 31 (1), 1-43, 2008
Machine Learning181 citationsNew integer linear programming approaches for course timetabling
N Boland, BD Hughes, LTG Merlot, PJ Stuckey
Computers & Operations Research 35 (7), 2209-2233, 2008
Research96 citationsExploration of networks using overview+ detail with constraint-based cooperative layout
T Dwyer, K Marriott, F Schreiber, P Stuckey, M Woodward, M Wybrow
IEEE Transactions on Visualization and Computer Graphics 14 (6), 1293-1300, 2008
Machine Learning83 citations- Research83 citations
Logic programming with satisfiability
M Codish, V Lagoon, PJ Stuckey
Theory and Practice of Logic Programming 8 (1), 121-128, 2008
Research48 citationsStructural search and retrieval using a tableau representation of protein folding patterns
AS Konagurthu, PJ Stuckey, AM Lesk
Bioinformatics 24 (5), 645-651, 2008
Optimization35 citationsProduct retrieval for grocery stores
P Nurmi, E Lagerspetz, W Buntine, P Floréen, J Kukkonen
Proceedings of the 31st annual international ACM SIGIR conference on …, 2008
Research35 citationsHM (X) type inference is CLP (X) solving
M Sulzmann, PJ Stuckey
Journal of Functional Programming 18 (2), 251-283, 2008
Bayesian Methods30 citationsGlobal difference constraint propagation for finite domain solvers
T Feydy, A Schutt, PJ Stuckey
Proceedings of the 10th international ACM SIGPLAN conference on Principles …, 2008
Machine Learning29 citationsFlexible, rule-based constraint model linearisation
S Brand, GJ Duck, J Puchinger, PJ Stuckey
International Symposium on Practical Aspects of Declarative Languages, 68-83, 2008
Machine Learning22 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.