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 73 of 99
2006
Topic Models in ALVIS
W Buntine, K Valtonen
The International Workshop on Intelligent Information Access (IIIA-2006 …, 2006
ResearchProceedings of Second Workshop on Open Source Infoemration Retrieval (OSIR 2006)
M Beigbeder, W Buntine, WG Yee
ACM, 2006
ResearchWorking Notes of the International Workshop on Intelligent Information Access (IIIA-2006)
W Buntine, H Tirri, P Myllymäki
ResearchWeb Search Technology-from Search to Semantic Search
L Zhou, W Buntine
The 1st Asian Semantic Web Conference-ASWC 2006, 3.-7.9. 2006, Beijing, China, 2006
OptimizationOSIR 2006: Second Workshop on Open Source Information Retrieval: August 10, 2006, Seattle, WA: in conjunction with the 2006 ACM SIGIR Conference
M Beigbeder, W Buntine, WG Yee
ResearchPreface to the" web search technology-from search to semantic search" workshop (SWET'06)
L Zhou, W Buntine
OptimizationPosters-ALVIS--Superpeer Semantic Search Engine
GS Pedersen, A Ardo, M Cromme, M Taylor, W Buntine
Lecture Notes in Computer Science 4172, 461-462, 2006
Optimization
2005
Discovery of minimal unsatisfiable subsets of constraints using hitting set dualization
J Bailey, PJ Stuckey
International Workshop on Practical Aspects of Declarative Languages, 174-186, 2005
Machine Learning276 citationsSystem and method for adaptive text recommendation
JJ Oliver, WL Buntine, G Roumeliotis
US Patent App. 11/927,450, 2008
Research204 citationsFast node overlap removal
T Dwyer, K Marriott, PJ Stuckey
International Symposium on Graph Drawing, 153-164, 2005
Research187 citationsDiscrete component analysis
W Buntine, A Jakulin
International Statistical and Optimization Perspectives Workshop" Subspace …, 2005
Research174 citationsA theory of overloading
PJ Stuckey, M Sulzmann
Acm transactions on programming languages and systems (toplas) 27 (6), 1216-1269, 2005
Research97 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.