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 48 of 99
2015
Package ‘rsnns’
C Bergmeir, JM Benítez
Research3 citations- Research3 citations
Research on domain-oriented latent policy lineage mining method
G Liu, W Buntine, X Yang, W Fu
2015 eighth international conference on internet computing for science and …, 2015
Machine Learning2 citationsAutomatic minimal-height table layout
M Bilauca, G Gange, P Healy, K Marriott, P Moulder, PJ Stuckey
INFORMS Journal on Computing 27 (3), 449-461, 2015
Research1 citationsOptimisation and Relaxation for Multiagent Planning in the Situation Calculus
TO Davies, AR Pearce, PJ Stuckey, H Søndergaard
Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015
Optimization1 citationsMaking Topic Models More Usable
W Buntine
Proceedings of the 2015 Workshop on Topic Models: Post-Processing and …, 2015
Research1 citationsDynamic capacity control and balancing in the medium voltage grid (doctoral consortium)
F De Nijs, MTJ Spaan, MM De Weerdt
AAMAS 2015: 14th International Conference on Autonomous Agents and …, 2015
ResearchTwo type extensions for the constraint modelling language MiniZinc
R Caballero Roldán, PJ Stuckey, Á Tenorio Fornés
Elsevier, 2015
Machine LearningUnsatisfiable Cores and Lower Bounding for Constraint Programming
N Downing, T Feydy, PJ Stuckey
arXiv preprint arXiv:1508.06096, 2015
Machine LearningPart I The Resource-Constrained Project Scheduling Problem 1 Shifts, Types, and Generation Schemes for Project Schedules......
R Kolisch, C Artigues, O Koné, P Lopez, M Mongeau, S Knust, A Agarwal, ...
Handbook on Project Management and Scheduling Vol. 2, 626, 2015
Machine LearningТом. 5. Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015.-Сер …
RA Aziz, G Chu, C Muise, P Stuckey, T Keller, F Geißer, A Ramdas, ...
Machine LearningConstraint Programming approach to the Steiner tree problem with side constraints using learning
D de Una, G Gange, P Schachte, PJ Stuckey
Machine Learning
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“Forecasting for Data Scientists”, a free 30-chapter video course by co-founder Dr Christoph Bergmeir, from fundamentals to advanced deep learning.