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 15 of 99
2022
Using MaxSAT for efficient explanations of tree ensembles
A Ignatiev, Y Izza, PJ Stuckey, J Marques-Silva
Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 3776-3785, 2022
Research93 citationsModel selection in reconciling hierarchical time series
M Abolghasemi, RJ Hyndman, E Spiliotis, C Bergmeir
Machine Learning 111 (2), 739-789, 2022
Forecasting37 citationsPruning vs XNOR-net: A comprehensive study of deep learning for audio classification on edge-devices
M Mohaimenuzzaman, C Bergmeir, B Meyer
IEEE Access 10, 6696-6707, 2022
Machine Learning35 citationsMulti-agent path finding for precedence-constrained goal sequences
H Zhang, J Chen, J Li, B Williams, S Koenig
International Joint Conference on Autonomous Agents and Multiagent Systems …, 2022
Machine Learning32 citationsAssociation between urine output and mortality in critically ill patients: a machine learning approach
AJ Heffernan, S Judge, SM Petrie, R Godahewa, C Bergmeir, D Pilcher, ...
Critical care medicine 50 (3), e263-e271, 2022
Machine Learning30 citationsProximal policy optimization based reinforcement learning for joint bidding in energy and frequency regulation markets
M Anwar, C Wang, F De Nijs, H Wang
2022 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2022
Machine Learning30 citationsLimref: Local interpretable model agnostic rule-based explanations for forecasting, with an application to electricity smart meter data
D Rajapaksha, C Bergmeir
Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 12098 …, 2022
Forecasting27 citationsSmooth perturbations for time series adversarial attacks
G Pialla, HI Fawaz, M Devanne, J Weber, L Idoumghar, PA Muller, ...
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 485-496, 2022
Forecasting25 citationsA divide and conquer algorithm for predict+ optimize with non-convex problems
AU Guler, E Demirović, J Chan, J Bailey, C Leckie, PJ Stuckey
Proceedings of the AAAI conference on artificial intelligence 36 (4), 3749-3757, 2022
Research22 citationsMachine learning to predict adverse outcomes after cardiac surgery: a systematic review and meta‐analysis
JC Penny‐Dimri, C Bergmeir, L Perry, L Hayes, R Bellomo, JA Smith
Machine Learning21 citationsFast optimal and bounded suboptimal euclidean pathfinding
B Shen, MA Cheema, DD Harabor, PJ Stuckey
Artificial Intelligence 302, 103624, 2022
Research20 citationsComparison and Evaluation of Methods for a Predict+ Optimize Problem in Renewable Energy
C Bergmeir, F de Nijs, A Sriramulu, M Abolghasemi, R Bean, J Betts, ...
arXiv preprint arXiv:2212.10723, 2022
Research19 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.