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 91 of 99
1995
ELS-programs and the efficient evaluation of non-stratified programs by transformation to ELS
DB Kemp, K Ramamohanarao, PJ Stuckey
International Conference on Deductive and Object-Oriented Databases, 91-108, 1995
Research20 citationsOn inductive inference of cyclic structures
MJ Maher, PJ Stuckey
Annals of mathematics and artificial intelligence 15 (2), 167-208, 1995
Bayesian Methods13 citationsPredicting engine parameters using the optical spectrum of the space shuttle main engine exhaust plume
A Srivastava, W Buntine
10th Computing in Aerospace Conference, 954, 1995
Machine Learning13 citationsAdvances in knowledge discovery and data mining
W Buntine, UM Fayyad, G Piatetsky-Shapiro, P Smyth
Graphical Models for Discovering Knowledge, AAAI Press, 59-82, 1995
Research10 citationsLinear Equation Solving for Constraint Logic Programming.
J Burg, PJ Stuckey, JCH Tai, RHC Yap
ICLP, 33-47, 1995
Machine Learning8 citationsIntelligent Instruments: Discovering How to Turn Spectral Data into Information.
WL Buntine, T Patel
KDD, 33-38, 1995
Research6 citationsImproved Analysis of Logic Programs using a Differential Approach
MG de la Banda
Department of Computer Science, University of Melbourne, 1995
Research5 citationsLearning in networks
WL Buntine
Research5 citationsSoftware for data analysis with graphical models: Basic tools
W Buntine, HS Roy
Fifth International Artificial Intelligence and Statistics Workshop, Ft …, 1995
Bayesian Methods2 citationsAn Optimizing Compiler for CLP (R)
AD Kelly¹
Principles and Practice of Constraint Programming: Proceedings 1520, 222, 1995
ResearchProceedings of the ICLP'95 Post-Conference Workshop on Abstract Interpretation of Logic Languages
M García de la Banda, G Janssens, P Stuckey
Science University of Tokyo, 1995
Research
1994
Operations for learning with graphical models
WL Buntine
Journal of artificial intelligence research 2, 159-225, 1994
Bayesian Methods929 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.