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 12 of 99
2023
From formal boosted tree explanations to interpretable rule sets
J Yu, A Ignatiev, PJ Stuckey
29th International Conference on Principles and Practice of Constraint …, 2023
Research8 citationsRobust educational dialogue act classifiers with low-resource and imbalanced datasets
J Lin, W Tan, ND Nguyen, D Lang, L Du, W Buntine, R Beare, G Chen, ...
International Conference on Artificial Intelligence in Education, 114-125, 2023
Research8 citationsCommon Pitfalls and Better Practices in Forecast Evaluation for Data Scientists.
C Bergmeir
Foresight: The International Journal of Applied Forecasting, 2023
Forecasting7 citationsDoes informativeness matter? Active learning for educational dialogue act classification
W Tan, J Lin, D Lang, G Chen, D Gašević, L Du, W Buntine
International Conference on Artificial Intelligence in Education, 176-188, 2023
Research7 citationsCausal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand
AN Prasanna, P Grecov, AD Weng, C Bergmeir
IEEE Transactions on Power Systems 39 (2), 3417-3430, 2023
Forecasting5 citationsAdaptively weighted audits of instant-runoff voting elections: AWAIRE
A Ek, PB Stark, PJ Stuckey, D Vukcevic
International Joint Conference on Electronic Voting, 35-51, 2023
Machine Learning5 citationsA constraint programming solution to the guillotine rectangular cutting problem
S Polyakovskiy, PJ Stuckey
Proceedings of the International Conference on Automated Planning and …, 2023
Machine Learning4 citationsChameleonIDE: Untangling Type Errors Through Interactive Visualization and Exploration
S Fu, T Dwyer, PJ Stuckey, J Wain, J Linossier
2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC …, 2023
Research4 citationsPaying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network
JC Penny-Dimri, C Bergmeir, CM Reid, J Williams-Spence, AD Cochrane, ...
Plos one 18 (8), e0289930, 2023
Machine Learning3 citationsDeep active audio feature learning in resource-constrained environments
M Mohaimenuzzaman, C Bergmeir, B Meyer
arXiv preprint arXiv:2308.13201, 2023
Machine Learning3 citationsA FastMap-Based Framework for Efficiently Computing Top-K Projected Centrality
A Li, P Stuckey, S Koenig, TKS Kumar
Research3 citationsLifted Sequential Planning with Lazy Constraint Generation Solvers
A Singh, M Ramirez, N Lipovetzky, PJ Stuckey
arXiv preprint arXiv:2307.08242, 2023
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.