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 55 of 99
2013
Structure based extended resolution for constraint programming
G Chu, PJ Stuckey
arXiv preprint arXiv:1306.4418, 2013
Machine Learning5 citationsInductive definitions in constraint programming
RA Aziz, PJ Stuckey, Z Somogyi
Proceedings of the Thirty-Sixth Australasian Computer Science Conference …, 2013
Machine Learning5 citationsStatistical inference of protein" LEGO bricks"
AS Konagurthu, L Allison, D Abramson, PJ Stuckey, AM Lesk
Bayesian Methods4 citationsFinite type extensions in Constraint Programming (extended version)
R Caballero, PJ Stuckey, A Tenorio-Fornés
Technical Report SIC-05/13, Facultad de Informática, Universidad Complutense …, 2013
Machine Learning4 citationsNew approaches in time series forecasting: methods, software and evaluation procedures
CN Bergmeir
Universidad de Granada, 2013
Forecasting3 citationsThose who cannot remember the past are condemned to repeat it
PJ Stuckey
International Conference on Principles and Practice of Constraint …, 2013
Research3 citationsFinite type extensions in constraint programming
R Caballero, PJ Stuckey, A Tenorio-Fornés
Proceedings of the 15th Symposium on Principles and Practice of Declarative …, 2013
Machine Learning3 citationsUnsatisfiable cores for constraint programming
N Downing, T Feydy, PJ Stuckey
arXiv preprint arXiv:1305.1690, 2013
Machine Learning2 citationsRsiopred: An R package for forecasting by exponential smoothing with model selection by a fuzzy multicriteria approach
C Bergmeir, JM Benítez, J Bermúdez, JV Segura, E Vercher
The R User Conference, useR! 2013 July 10-12 2013 University of Castilla-La …, 2013
ForecastingStatistical Inference of a canonical dictionary of protein substructural fragments
AS Konagurthu, AM Lesk, D Abramson, PJ Stuckey, L Allison
arXiv preprint arXiv:1310.1462, 2013
Bayesian Methods- Machine Learning
Automated Design of Search with Composability.
A Sabharwal, H Samulowitz, T Schrijvers, PJ Stuckey, G Tack
AAAI (Late-Breaking Developments), 2013
Optimization
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.