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 90 of 99
1996
Models for using stochastic constraint solvers in constraint logic programming
PJ Stuckey, V Tam
International Symposium on Programming Language Implementation and Logic …, 1996
Machine Learning11 citationsOptimizing bottom-up evaluation of constraint queries
DB Kemp, PJ Stuckey
The Journal of logic programming 26 (1), 1-30, 1996
Machine Learning10 citationsUsing stochastic methods to guide search in CLP: a preliminary report
JHM Lee, HF Leung, PJ Stuckey, VWL Tam, HW Won
Annual Asian Computing Science Conference, 43-52, 1996
Optimization6 citationsLow-contact learning in a first year programming course
R Johnston, A Moffat, H Søndergaard, P Stuckey
Proceedings of the 1st Australasian conference on Computer science education …, 1996
Research5 citationsUsing Stochastic Solvers in Constraint Logic Programming
PJ Stuckey, VWL Tam
AUSTRALIAN COMPUTER SCIENCE COMMUNICATIONS 18, 174-183, 1996
Machine Learning1 citationsTwo applications of an incremental analysis engine for (constraint) logic programs
AD Kelly, K Marriott, H SØndergaard, PJ Stuckey
International Static Analysis Symposium, 385-386, 1996
Machine LearningEngine for (Constraint) Logic Programs
AD Kelly¹, K Marriott¹, H Søndergaard, PJ Stuckey
Proceedings 864, 385, 1996
Machine Learning
1995
- Machine Learning127 citations
Bottom-up evaluation and query optimization of well-founded models
DB Kemp, D Srivastava, PJ Stuckey
Theoretical computer science 146 (1-2), 145-184, 1995
Optimization79 citationsA unit two variable per inequality integer constraint solver for constraint logic programming
W Harvey, PJ Stuckey
Department of Computer Science, University of Melbourne, 1995
Machine Learning67 citationsEfficient Analysis of Logic Programs with Dynamic Scheduling.
MJG de la Banda, K Marriott, PJ Stuckey
ILPS 95, 417-431, 1995
Optimization40 citationsAn optimizing compiler for CLP (ℛ)
AD Kelly, A Macdonald, K Marriott, H Søndergaard, PJ Stuckey, RHC Yap
Research25 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.