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 42 of 99
2017
Linear regression
N Quadrianto, WL Buntine
Encyclopedia of Machine Learning and Data Mining, 747-750, 2017
Research10 citationsTowards computing victory margins in STV elections
M Blom, PJ Stuckey, VJ Teague
arXiv preprint arXiv:1703.03511, 2017
Research8 citationsA Benders decomposition approach to deciding modular linear integer arithmetic
B Kafle, G Gange, P Schachte, H Søndergaard, PJ Stuckey
International conference on theory and applications of satisfiability …, 2017
Research5 citationsStatistical compression of protein folding patterns for inference of recurrent substructural themes
R Subramanian, L Allison, PJ Stuckey, MG De La Banda, D Abramson, ...
2017 Data Compression Conference (DCC), 340-349, 2017
Bayesian Methods5 citationsRange-consistent forbidden regions of Allen’s relations
N Beldiceanu, M Carlsson, A Derrien, C Prud’Homme, A Schutt, ...
Research3 citationsLinear discriminant
N Quadrianto, WL Buntine
Encyclopedia of Machine Learning and Data Mining, 745-747, 2017
Research3 citationsGraphical models
J McAuley, T Caetano, WL Buntine
Encyclopedia of Machine Learning and Data Mining, 584-592, 2017
Bayesian Methods3 citationsMinimizing landscape resistance for habitat conservation
D de Una, G Gange, P Schachte, PJ Stuckey
International Conference on AI and OR Techniques in Constraint Programming …, 2017
Research2 citations- Bayesian Methods2 citations
Backoff methods for estimating parameters of a Bayesian network
W Buntine
Advanced Methodologies for Bayesian Networks, 3-3, 2017
Bayesian Methods1 citations
2016
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
C Bergmeir, RJ Hyndman, JM Benítez
Research479 citationsTwitter-network topic model: A full Bayesian treatment for social network and text modeling
KW Lim, C Chen, W Buntine
arXiv preprint arXiv:1609.06791, 2016
Bayesian Methods77 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.