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 72 of 99
2006
Tagging in Peer-to-Peer Wikipedia: A method to induce cooperation
JE Fokker, W Buntine, JA Pouwelse, H De Ridder, PH Westendorp
29th Annual International ACM SIGIR Conference on Research & Development on …, 2006
Research4 citations- Research2 citations
Open source search and research
M Beigbeder, W Buntine, WG Yee
Proceedings of the 2006 international workshop on Research issues in digital …, 2006
Optimization2 citationsALVIS–superpeer semantic search engine–ECDL 2006 demo submission
GS Pedersen, A Ardö, M Cromme, M Taylor, W Buntine
Optimization2 citations- Research2 citations
Type inference via constraint abduction for EADTs
M Sulzmann, T Schrijvers, PJ Stuckey
Manuscript, April, 2006
Machine Learning1 citations- Research1 citations
Improving PARMA trailing
T Schrijvers, B Demoen, MG De la Banda, PJ Stuckey
Theory and Practice of Logic Programming 6 (6), 609-644, 2006
Machine LearningTR-2006013: The Dom Event and Its Use in Implementing Constraint Propagators
NF Zhou, M Wallace, PJ Stuckey
Machine LearningEditors' Introduction to the Special Issue" Learning in Web Search.".
S Bloehdorn, W Buntine, A Hotho
OptimizationProceedings of the International Workshop on Intelligent Information Access (IIIA-2006)
W Buntine, H Tirri, P Myllymäki
Unknown Publisher, 2006
ResearchSpecial issue on" Learning in Web Search"
S Bloehdorn, W Buntine, A Hotho
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.