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 26 of 99
2020
Dynamic Programming for Predict+ Optimise.
E Demirovic, PJ Stuckey, T Guns, J Bailey, C Leckie, K Ramamohanarao, ...
Proceedings of the AAAI Conference on Artificial Intelligence 34 (02), 1444-1451, 2020
Research38 citationsVariational autoencoders for sparse and overdispersed discrete data
H Zhao, P Rai, L Du, W Buntine, D Phung, M Zhou
International conference on artificial intelligence and statistics, 1684-1694, 2020
Research30 citationsEuclidean pathfinding with compressed path databases
B Shen, MA Cheema, DD Harabor, PJ Stuckey
International Joint Conference on Artificial Intelligence-Pacific Rim …, 2020
Research23 citationsCore-guided and core-boosted search for CP
G Gange, J Berg, E Demirović, PJ Stuckey
International Conference on Integration of Constraint Programming …, 2020
Optimization21 citationsExact approaches to the multi-agent collective construction problem
E Lam, PJ Stuckey, S Koenig, TKS Kumar
International Conference on Principles and Practice of Constraint …, 2020
Multi-Agent Systems21 citationsBayesian network classifiers using ensembles and smoothing
H Zhang, F Petitjean, W Buntine
Knowledge and Information Systems 62 (9), 3457-3480, 2020
Bayesian Methods21 citationsTowards accurate predictions and causal ‘what-if’analyses for planning and policy-making: A case study in emergency medical services demand
K Bandara, C Bergmeir, S Campbell, D Scott, D Lubman
Forecasting18 citationsNutmeg: a MIP and CP hybrid solver using branch-and-check
E Lam, G Gange, PJ Stuckey, P Van Hentenryck, JJ Dekker
SN Operations Research Forum 1 (3), 22, 2020
Research18 citationsSolving satisfaction problems using large-neighbourhood search
G Björdal, P Flener, J Pearson, PJ Stuckey, G Tack
International Conference on Principles and Practice of Constraint …, 2020
Optimization18 citationsMachine learning after the deep learning revolution
W Buntine
Frontiers of Computer Science 14 (6), 146320, 2020
Machine Learning17 citationsOptimal decision lists using SAT
J Yu, A Ignatiev, PL Bodic, PJ Stuckey
arXiv preprint arXiv:2010.09919, 2020
Research16 citationsOnline computation of euclidean shortest paths in two dimensions
R Hechenberger, PJ Stuckey, D Harabor, P Le Bodic, MA Cheema
Proceedings of the International Conference on Automated Planning and …, 2020
Research16 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.