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 38 of 99
2018
Image constrained blockmodelling: a constraint programming approach
M Ganji, J Chan, PJ Stuckey, J Bailey, C Leckie, K Ramamohanarao, ...
Proceedings of the 2018 SIAM International Conference on Data Mining, 19-27, 2018
Machine Learning7 citationsPackage forecast-the comprehensive R archive network
R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
Jun, 2018
Forecasting6 citationsforecast: Forecasting functions for time series and linear models (version 8.)
R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
R package, 2018
Forecasting6 citationsA study of evacuation planning for wildfires
C Artigues, E Hébrard, Y Pencolé, A Schutt, PJ Stuckey
The Seventeenth International Workshop on Constraint Modelling and …, 2018
Optimization6 citationsDistinguishing question subjectivity from difficulty for improved crowdsourcing
Y Jin, M Carman, Y Zhu, W Buntine
Asian Conference on Machine Learning, 192-207, 2018
Research6 citationsExperiments with learning graphical models on text
J Capdevila, H Zhao, F Petitjean, W Buntine
Behaviormetrika 45 (2), 363-387, 2018
Bayesian Methods6 citationsA left-to-right algorithm for likelihood estimation in gamma-poisson factor analysis
J Capdevila, J Cerquides, J Torres, F Petitjean, W Buntine
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018
Research6 citationsSequential precede chain for value symmetry elimination
G Gange, PJ Stuckey
International Conference on Principles and Practice of Constraint …, 2018
Machine Learning5 citationsBreaking symmetries with lex implications
M Codish, T Ehlers, G Gange, A Itzhakov, PJ Stuckey
International Symposium on Functional and Logic Programming, 182-197, 2018
Research5 citationsLocal-style search in the linear MaxSAT algorithm: A computational study of solution-based phase saving
E Demirovic, P Stuckey
Pragmatics of SAT workshop, 2018
Optimization5 citationsforecast: Forecasting functions for time series and linear models. Software, R package
RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
Forecasting3 citationsSemi-supervised blockmodelling with pairwise guidance
M Ganji, J Chan, PJ Stuckey, J Bailey, C Leckie, K Ramamohanarao, ...
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018
Machine Learning3 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.