This repository serves as a curated reference for the domain of forecast reconciliation. It aims to contain an extensive collection of academic papers, articles, software tools, and educational resources. Ideal for researchers, analysts, and practitioners seeking to improve the consistency and precision of forecasting methodologies.

We wish to express our deep appreciation to the authors of the paper "Forecast reconciliation: A review" - George Athanasopoulos, Rob J Hyndman, Nikolaos Kourentzes, and Anastasios Panagiotelis - for providing their BibTeX file, which served as the cornerstone of this repository. Their paper serves as an invaluable resource with its comprehensive and insightful analysis of the forecast reconciliation field, providing a thorough overview of the existing literature and highlighting key advancements and research trends.

The remainder of the README is organized as follows: the Introduction collects entry points and reviews; the three core sections cover the main reconciliation frameworks (Cross-sectional, Temporal, and Cross-temporal). A Software section catalogs open-source implementations in R and Python. Beyond forecasting and reconciliation gathers closely related foundations (aggregation, benchmarking, temporal distribution, accounting constraints, etc.). Finally, Thesis section includes dissertations in the area.

If you find this repository useful in your research or applications, please cite it as:

Girolimetto, D., & Yang, YF. (2025). Awesome Forecast Reconciliation: A Curated Reference List. https://github.com/danigiro/awesome-forecast-reconciliation

@misc{awesomeFR,
  title        = {Awesome Forecast Reconciliation: A Curated Reference List},
  author       = {Girolimetto, Daniele and Yang, Yangzhuoran Fin},
  year         = {2025},
  url          = {https://github.com/danigiro/awesome-forecast-reconciliation}
}

• Introduction
• Cross-sectional framework
• Temporal framework
• Cross-temporal framework
• Software
• Beyond forecasting and reconciliation
• Thesis

1. Chapter 11 Forecasting Hierarchical and grouped time series by Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice, 3rd edition (2021). [url]
2. Notation for forecast reconciliation by Rob J Hyndman (2022). [url]
3. Editorial: Innovations in Hierarchical Forecasting by Athanasopoulos, George, Rob J. Hyndman, Nikolaos Kourentzes, and Anastasios Panagiotelis, International Journal of Forecasting (2024). [doi]
4. Forecast reconciliation: A review by Athanasopoulos, George, Rob J. Hyndman, Nikolaos Kourentzes, and Anastasios Panagiotelis, International Journal of Forecasting (2024). [doi]

1. Data aggregation and information loss by Guy H. Orcutt, Harold W. Watts, John B. Edwards, The American Economic Review (1968).
2. The Effect of Aggregation on Prediction in the Autoregressive model by Takeshi Amemiya, Roland Y. Wu, Journal of the American Statistical Association (1972). [doi]
3. Top-down versus bottom-up forecasting strategies by Albert B Schwarzkopf, Richard J Tersine, John S Morris, International Journal of Production Research (1988). [doi]
4. Aggregation vs disaggregation in forecasting construction activities by Pekka Ilmakunnas, Disaggregation in econometric modelling (1990).
5. Top-down or bottom-up: aggregate versus disaggregate extrapolations by B J Dangerfield, J S Morris, International Journal of Forecasting (1992). [doi]
6. A simple view of top-down vs bottom-up forecasting by L Lapide, Journal of Business Forecasting Methods and Systems (1998).
7. An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation by G Fliedner, Computers and Operations Research (1999).
8. Hierarchical forecasting: issues and use guidelines by Gene Fliedner, Industrial Management & Data Systems (2001). [doi]
9. The impact of aggregation level on forecasting performance by Giulio Zotteri, Matteo Kalchschmidt, Federico Caniato, International Journal of Production Economics (2005). [doi]
10. A model for selecting the appropriate level of aggregation in forecasting processes by Giulio Zotteri, Matteo Kalchschmidt, International Journal of Production Economics (2007). [doi]
11. Top-down or bottom-up forecasting? by Peter Wanke, Eduardo Saliby, Pesquisa Operacional (2007).
12. Forecasting item-level demands: an analytical evaluation of top-down versus bottom-up forecasting in a production-planning framework by H Widiarta, S Viswanathan, R Piplani, IMA Journal of Management Mathematics (2008). [doi]
13. Hierarchical estimation as a basis for hierarchical forecasting by L W G Strijbosch, R M J Heuts, J J A Moors, IMA Journal of Management Mathematics (2008). [doi]
14. Hierarchical forecasts for Australian domestic tourism by George Athanasopoulos, Roman A Ahmed, Rob J Hyndman, International Journal of Forecasting (2009). [doi]
15. Multi-horizon inflation forecasts using disaggregated data by Carlos Capistr\a'an, Christian Constandse, Manuel Ramos-Francia, Economic Modelling (2010). [doi]
16. Top-down versus bottom-up demand forecasts: The value of shared point-of-sale data in the retail supply chain by Brent D. Williams, Matthew A. Waller, Journal of Business Logistics (2011). [doi]
17. Optimal combination forecasts for hierarchical time series by Rob J. Hyndman, Roman A. Ahmed, George Athanasopoulos, Han Lin Shang, Computational Statistics & Data Analysis (2011). [doi]
18. Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting by Ivette Luna, Rosangela Ballini, International Journal of Forecasting (2011). [doi]
19. The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study by Seongmin Moon, Christian Hicks, Andrew Simpson, International Journal of Production Economics (2012).
20. Sample-Based Forecasting Exploiting Hierarchical Time Series by Ulrike Fischer, Frank Rosenthal, Wolfgang Lehner, Proceedings of the 16th International Database Engineering Applications Sysmposium (2012). [doi]
21. Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework by Giacomo Sbrana, Andrea Silvestrini, International Journal of Production Economics (2013). [doi]
22. Aggregate vs. disaggregate forecast: Case of Hong Kong by Shui Ki Wan, Shin Huei Wang, Chi Keung Woo, Annals of Tourism Research (2013). [doi]
23. Efficient Forecasting for Hierarchical Time Series by L Dannecker, R Lorenz, P R\osch, W Lehner, G Hackenbroich, CIKM '13 Proceedings of the 22nd ACM international conference on Information & Knowledge Management (2013). [doi] [url]
24. Variational Bayesian inference for forecasting hierarchical time series by Mijung Park, Marcel Nassar, International Conference on Machine Learning, Workshop on divergence methods for probabilistic inference (2014). [url]
25. Optimally reconciling forecasts in a hierarchy by R J Hyndman, George Athanasopoulos, Foresight: International Journal of Applied Forecasting (2014). [url]
26. Non-stationary demand forecasting by cross-sectional aggregation by Bahman Rostami-Tabar, Mohamed Zied Babai, Yves Ducq, Aris Syntetos, International Journal of Production Economics (2015).
27. Improving forecasts for noisy geographic time series by S H Huddlestone, J H Porter, D E Brown, Journal of Business Research (2015).
28. Forecast UPC-level FMCG demand, Part II: Hierarchical reconciliation by Dazhi Yang, Gary S.W. Goh, Siwei Jiang, Allan N. Zhang, Orkan Akcan, Proceedings - 2015 IEEE International Conference on Big Data (2015). [doi]
29. Game-theoretically optimal reconciliation of contemporaneous hierarchical time series forecasts by Tim van Erven, Jairo Cugliari, Modeling and Stochastic Learning for Forecasting in High Dimension (2015). [url]
30. The sum and its parts: judgmental hierarchical forecasting by Mirko Kremer, Enno Siemsen, Douglas J Thomas, Management Science (2016). [doi]
31. Hierarchical Time Series Forecast in Electrical Grids by Vȧnia Almeida, Rita Ribeiro, Joȧo Gama, Information Science and Applications (ICISA) (2016). [doi]
32. Forecast UPC-level FMCG demand, Part III: Grouped reconciliation by Dazhi Yang, Gary S.W. Goh, Siwei Jiang, Allan N. Zhang, Proceedings - 2016 IEEE International Conference on Big Data (2016). [doi]
33. Distributions of forecasting errors of forecast combinations: implications for inventory management by Devon K Barrow, Nikolaos Kourentzes, International Journal of Production Economics (2016).
34. Fast computation of reconciled forecasts for hierarchical and grouped time series by Rob J. Hyndman, Alan J. Lee, Earo Wang, Computational Statistics & Data Analysis (2016). [doi]
35. Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods by Han Lin Shang, Population Resarch Policy Review (2016). [arXiv]
36. Grouped multivariate and functional time series forecasting: An application to annuity pricing by Han Lin Shang, Steven Haberman, Insurance: Mathematics and Economics (2017). [doi]
37. Regularization in hierarchical time series forecasting with application to electricity smart meter data by Souhaib Ben Taieb, Jiafan Yu, Mateus Neves Barreto, Ram Rajagopal, 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (2017). [url]
38. Reconciling solar forecasts: Geographical hierarchy by Dazhi Yang, Hao Quan, Vahid R. Disfani, Licheng Liu, Solar Energy (2017). [doi]
39. Aggregated moving functional median in robust prediction of hierarchical functional time series - an application to forecasting web portal users behaviors by Daniel Kosiorowski, Dominik Mielczarek, Jerzy P. Rydlewski (2017). [arXiv]
40. Coherent probabilistic forecasts for hierarchical time series by Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman, Proceedings of the 34th International Conference on Machine Learning (2017). [url] [probabilistic]
41. Grouped functional time series forecasting: an application to age-specific mortality rates by Han Lin Shang, Rob J. Hyndman, Journal of Computational and Graphical Statistics (2017). [doi] [probabilistic]
42. A Bayesian model for forecasting hierarchically structured time series by Julie Novak, Scott McGarvie, Beatriz Etchegaray Garcia (2017). [arXiv] [probabilistic]
43. Hierarchical accounting variables forecasting by deep learning methods by Mengke Qiao, Ke-Wei Huang, ICIS 2018 Proceedings 7 (2018). [url]
44. Supply chain decision support systems based on a novel hierarchical forecasting approach by Marco A Villegas, Diego J Pedregal, Decision Support Systems (2018).
45. Using quadratic programming to optimally adjust hierarchical load forecasting by Y Zhang, J Wang, T Zhao, IEEE Transactions on Power Systems (2018). [doi]
46. Generalized exponential smoothing in prediction of hierarchical time series by Daniel Kosiorowski, Dominik Mielczarek, Jerzy Rydlewski, Ma\lgorzata Snarska, Statistics in Transition, New series (2018). [doi]
47. Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for the Day and Night Air Pollution in Silesia Region---A Critical Overview by Daniel Kosiorowski, Dominik Mielczarek, Jerzy P. Rydlewski, Central European Journal of Economic Modelling and Econometrics (2018). [url]
48. A hierarchical approach to probabilistic wind power forecasting by Ciaran Gilbert, Jethro Browell, David McMillan, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (2018). [doi] [probabilistic]
49. Coherent probabilistic solar power forecasting by Hossein Panamtash, Qun Zhou, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (2018). [doi] [probabilistic]
50. Assessing the performance of hierarchical forecasting methods on the retail sector by José Manuel Oliveira, Patrícia Ramos, Entropy (2019). [doi]
51. A self-supervised approach to hierarchical forecasting with applications to groupwise synthetic controls by Konstantin Mishchenko, Mallory Montgomery, Federico Vaggi, https://arxiv.org/abs/1906.10586 (2019).
52. Hierarchical time series forecasting via support vector regression in the European travel retail industry by Juan Pablo Karmy, Sebastián Maldonado, Expert Systems with Applications (2019). [doi]
53. Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization by Shanika L. Wickramasuriya, George Athanasopoulos, Rob J. Hyndman, Journal of the American Statistical Association (2019). [doi]
54. Machine learning applications in time series hierarchical forecasting by Mahdi Abolghasemi, Rob J Hyndman, Garth Tarr, Christoph Bergmeir (2019). [arXiv]
55. Regularized regression for hierarchical forecasting without unbiasedness conditions by Souhaib Ben Taieb, Bonsoo Koo, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019). [doi]
56. Multi-task learning method for hierarchical time series forecasting by Maoxin Yang, Qinghua Hu, Yun Wang, Artificial Neural Networks and Machine Learning -- ICANN 2019: Text and Time Series (2019). [doi]
57. Distributed Reconciliation in Day-Ahead Wind Power Forecasting by Li Bai, Pierre Pinson, Energies (2019). [doi]
58. A bottom-up Bayesian extension for long term electricity consumption forecasting by F L C da Silva, F L Cyrino Oliveira, R C Souza, Energy (2019). [doi] [url]
59. A forecast reconciliation approach to cause-of-death mortality modeling by Han Li, Hong Li, Yang Lu, Anastasios Panagiotelis, Insurance: Mathematics and Economics (2019).
60. Analyzing mortality bond indexes via hierarchical forecast reconciliation by Han Li, Qihe Tang, ASTIN Bulletin (2019). [doi]
61. Day-ahead hierarchical probabilistic load forecasting with linear quantile regression and empirical copulas by Tianhui Zhao, Jianxue Wang, Yao Zhang, IEEE Access (2019). [doi] [probabilistic]
62. Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting by Tao Hong, Jingrui Xie, Jonathan Black, International Journal of Forecasting (2019). [doi] [probabilistic]
63. Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting by Cameron Roach, International Journal of Forecasting (2019). [doi] [probabilistic]
64. Prediction of hierarchical time series using structured regularization and its application to artificial neural networks by Tomokaze Shiratori, Ken Kobayashi, Yuichi Takano, PLOS ONE (2020). [doi]
65. Hierarchical forecasting by George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J Hyndman, Mohamed Affan, Macroeconomic Forecasting in the Era of Big Data (2020). [doi]
66. Optimal non-negative forecast reconciliation by Shanika L. Wickramasuriya, Berwin A. Turlach, Rob J. Hyndman, Statistics and Computing (2020). [doi]
67. Forecasting hierarchical time series with a regularized embedding space by Jeffrey L. Gleason, MileTS '20: 6th KDD Workshop on Mining and Learning from Time Series 2020 (2020). [url]
68. Fully reconciled GDP forecasts from income and expenditure sides by Luisa Bisaglia, Tommaso Di Fonzo, Daniele Girolimetto, Book of Short Papers SIS 2020 (2020). [arXiv]
69. Forecasting of cohort fertility under a hierarchical Bayesian approach by Joanne Ellison, Erengul Dodd, Jonathan J Forster, Journal of the Royal Statistical Society, Series A, (2020). [doi]
70. Hierarchical demand forecasting benchmark for the distribution grid by Lorenzo Nespoli, Vasco Medici, Kristijan Lopatichki, Fabrizio Sossan, Electric Power Systems Research (2020). [doi]
71. Reconciling solar forecasts: Probabilistic forecasting with homoscedastic Gaussian errors on a geographical hierarchy by Gokhan Mert Yagli, Dazhi Yang, Dipti Srinivasan, Solar Energy (2020). [doi] [probabilistic]
72. Reconciling solar forecasts: Probabilistic forecast reconciliation in a nonparametric framework by Dazhi Yang, Solar Energy (2020). [doi] [probabilistic]
73. Stochastic coherency in forecast reconciliation by Kandrika F. Pritularga, Ivan Svetunkov, Nikolaos Kourentzes, International Journal of Production Economics (2021). [doi]
74. Deep LSTM-based transfer learning approach for coherent forecasts in hierarchical time series by Alaa Sagheer, Hala Hamdoun, Hassan Youness, Sensors (2021).
75. Assessing mortality inequality in the U.S.: What can be said about the future? by Han Li, Rob J. Hyndman, Insurance: Mathematics and Economics (2021). [doi]
76. Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team by Nikolaos Kourentzes, Andrea Saayman, Philippe Jean-Pierre, Davide Provenzano, Mondher Sahli, Neelu Seetaram, Serena Volo, Annals of Tourism Research (2021).
77. Forecast reconciliation: A geometric view with new insights on bias correction by Anastasios Panagiotelis, George Athanasopoulos, Puwasala Gamakumara, Rob J. Hyndman, International Journal of Forecasting (2021). [doi]
78. Properties of point forecast reconciliation approaches by Shanika L Wickramasuriya (2021). [arXiv]
79. Forecasting Swiss exports using Bayesian forecast reconciliation by Florian Eckert, Rob J. Hyndman, Anastasios Panagiotelis, European Journal of Operational Research (2021). [doi]
80. A trainable reconciliation method for hierarchical time-series by Davide Burba, Trista Chen (2021). [arXiv]
81. Understanding forecast reconciliation by Ross Hollyman, Fotios Petropoulos, Michael E. Tipping, European Journal of Operational Research (2021). [doi]
82. A machine learning approach for forecasting hierarchical time series by Paolo Mancuso, Veronica Piccialli, Antonio M. Sudoso, Expert Systems with Applications (2021). [doi]
83. Hierarchical probabilistic forecasting of electricity demand with smart meter data by Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman, Journal of the American Statistical Association (2021). [doi] [probabilistic]
84. Improving Probabilistic Infectious Disease Forecasting Through Coherence by G C Gibson, K R Moran, N G Reich, D Osthus, PLoS computational biology (2021). [doi] [url] [probabilistic]
85. End-to-end learning of coherent probabilistic forecasts for hierarchical time series by Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski, Proceedings of the 38th International Conference on Machine Learning, PMLR 139 (2021). [url]
    • See Software for the implementation in the GluonTS package. [probabilistic]
86. Probabilistic reconciliation of hierarchical forecast via Bayes' rule by Giorgio Corani, Dario Azzimonti, Jo~ao P. S. C. Augusto, Marco Zaffalon, Machine Learning and Knowledge Discovery in Databases (2021). [doi] [probabilistic]
87. An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations by L Buzna, P De Falco, G Ferruzzi, S Khormali, D Proto, N Refa, M Straka, G van der Poele, Applied energy (2021). [doi] [url] [probabilistic]
88. Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression by Olivier Sprangers, Sebastian Schelter, Maarten de Rijke, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2021). [doi] [probabilistic]
89. Simultaneously reconciled quantile forecasting of hierarchically related time series by Xing Han, Sambarta Dasgupta, Joydeep Ghosh, Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) (2021). [arXiv] [probabilistic]
90. Automatic hierarchical time-series forecasting using Gaussian processes by Luis Roque, Luis Torgo, Carlos Soares, Engineering Proceedings (2021). [doi] [probabilistic]
91. Hierarchically regularized deep forecasting by Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das (2021). [arXiv] [probabilistic]
92. End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation by Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yangfei Zheng, Lei Lei, Yun Hu, 2022 IEEE International Conference on Data Mining Workshops (2022). [arXiv]
93. Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates by Gary Koop, Stuart Mcintyre, James Mitchell, Aubrey Poon, International Journal of Forecasting (2022). [url]
94. Fast forecast reconciliation using linear models by Mahsa Ashouri, Rob J. Hyndman, Galit Shmueli, Journal of Computational & Graphical Statistics (2022). [doi]
95. Forecast combination-based forecast reconciliation: Insights and extensions by Tommaso Di Fonzo, Daniele Girolimetto, International Journal of Forecasting (2022). [doi] [arXiv]
96. Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions by Mahdi Abolghasemi, Garth Tarr, Christoph Bergmeir, International Journal of Forecasting (2022).
97. Model selection in reconciling hierarchical time series by Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis, Christoph Bergmeir, Machine Learning (2022). [doi]
98. Hierarchical forecasting with a top-down alignment of independent level forecasts by Matthias Anderer, Feng Li, International Journal of Forecasting (2022). [doi]
99. Hierarchical Forecasting for Aggregated Curves with an Application to Day-Ahead Electricity Price Auctions. by Ghelasi, Paul, and Florian Ziel International Journal of Forecasting (2022). [doi]
100. Hierarchical Mortality Forecasting with EVT Tails: An Application to Solvency Capital Requirement., by Li, Han, and Hua Chen, International Journal of Forecasting (2022). [doi]
101. Forecasting Australian Fertility by Age, Region, and Birthplace. by Yang, Yang, Han Lin Shang, and James Raymer, International Journal of Forecasting (2022). [doi]
102. Online hierarchical forecasting for power consumption data by Margaux Brégėre, Malo Huard, International Journal of Forecasting (2022). [doi]
103. Forecasting unemployment in Brazil: A robust reconciliation approach using hierarchical data by Maurício Franca Lila, Erick Meira, Fernando Luiz Cyrino Oliveira, Socio-Economic Planning Sciences (2022). [doi]
104. Forecasting hierarchical time series in supply chains: an empirical investigation by Dejan Mircetic, Bahman Rostami-Tabar, Svetlana Nikolicic, Marinko Maslaric, International Journal of Production Research (2022). [doi]
105. Fully reconciled probabilistic GDP forecasts from income and expenditure sides by Tommaso Di Fonzo, Daniele Girolimetto, Book of Short Papers SIS 2022 (2022). [url] [arXiv] [probabilistic]
106. Probabilistic reconciliation of count time series by Giorgio Corani, Dario Azzimonti, Nicolo Rubattu (2022). [doi] [arXiv] [probabilistic]
107. Probabilistic hierarchical forecasting with deep Poisson mixtures by Kin G Olivares, O Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, Lee Dicker (2022). [arXiv] [probabilistic]
108. Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series by Ioannis Nasios, Konstantinos Vogklis, International Journal of Forecasting (2022). [doi] [probabilistic]
109. A hybrid approach with step-size aggregation to forecasting hierarchical time series by Hakeem-Ur- Rehman, Guohua Wan, Raza Rafique, Journal of forecasting (2023). [doi] [url]
110. Pooling information across levels in hierarchical time series forecasting via kernel methods by Juan Pablo Karmy, Julio López, Sebastián Maldonado, Expert Systems with Applications (2023). [doi]
111. On the evaluation of hierarchical forecasts by G. Athanasopoulos, N. Kourentzes, International Journal of Forecasting (2023).
112. Optimal reconciliation with immutable forecasts by Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis, Feng Li, European Journal of Operational Research (2023). [doi]
113. Hierarchical Transfer Learning with Applications to Electricity Load Forecasting. by Antoniadis, Anestis, Solenne Gaucher, and Yannig Goude, International Journal of Forecasting (2023). [doi]
114. Optimal Hierarchical EWMA Forecasting. by Sbrana, Giacomo, and Matteo Pelagatti, International Journal of Forecasting (2023). [doi]
115. Reconciliation of wind power forecasts in spatial hierarchies by Mads E Hansen, Nystrup Peter, Jan K M\oller, Madsen Henrik, Wind Energy (2023).
116. Probabilistic forecast reconciliation: properties, evaluation and score optimisation by Anastasios Panagiotelis, Puwasala Gamakumara, George Athanasopoulos, Rob J Hyndman, European Journal of Operational Research (2023). [doi] [probabilistic]
117. Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting by Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen (2023). [doi] [arXiv] [probabilistic]
118. Probabilistic Forecast Reconciliation under the Gaussian Framework by Shanika L. Wickramasuriya, Journal of Business and Economic Statistics (2023). [doi] [url] [arXiv] [probabilistic]
119. Probabilistic forecasts using expert judgement: the road to recovery from COVID-19 by G Athanasopoulos, R J Hyndman, N Kourentzes, M. O'Hara-Wild, Journal of Travel Research (2023). [doi] [probabilistic]
120. Do we want coherent hierarchical forecasts, or minimal MAPEs or MAEs? (We won't get both!) by Stephan Kolassa, International Journal of Forecasting (2023). [probabilistic]
121. Point and probabilistic forecast reconciliation for general linearly constrained multiple time series by Daniele Girolimetto, Tommaso Di Fonzo, Statistical Methods & Applications (2023). [doi] [github] [arXiv] [probabilistic]
122. Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand by Zheng, Kedi, Hanwei Xu, Zeyang Long, Yi Wang, and Qixin Chen, IEEE Transactions on Industry Applications (2023). [doi] [probabilistic]
123. Multivariate Online Linear Regression for Hierarchical Forecasting. by Hihat, Massil, Guillaume Garrigos, Adeline Fermanian, and Simon Bussy (2024). [arXiv]
124. Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering. by Raffaele Mattera, George Athanasopoulos, Rob J. Hyndman Quantitative Finance (2024) [doi] [github]
125. Forecast Linear Augmented Projection (FLAP): A free lunch to reduce forecast error variance. Yangzhuoran Fin Yang, George Athanasopoulos, Rob J Hyndman, Anastasios Panagiotelis (2024). [arXiv] [github]
126. Exploiting intraday decompositions in Realized Volatility forecasting: a forecast reconciliation approach. Massimiliano Caporin, Tommaso Di Fonzo, Daniele Girolimetto, Journal of Financial Econometrics (2024). [doi] [arXiv]
127. Coherent forecast combination for linearly constrained multiple time series. by Daniele Girolimetto, Tommaso Di Fonzo, (2024). [arXiv]
128. Optimal Forecast Reconciliation with Time Series Selection. by Wang, Xiaoqian, Rob J. Hyndman, and Shanika L. Wickramasuriya, European J Operational Research (2025). [doi] [url] [github]

1. Forecasting with temporal hierarchies by George Athanasopoulos, Rob J. Hyndman, Nikolaos Kourentzes, Fotios Petropoulos, European Journal of Operational Research (2017). [doi]
2. Reconciling solar forecasts: Temporal hierarchy by Dazhi Yang, Hao Quan, Vahid R. Disfani, Carlos D. Rodríguez-Gallegos, Solar Energy (2017). [doi]
3. Improving the forecasting performance of temporal hierarchies by Evangelos Spiliotis, Fotios Petropoulos, Vassilios Assimakopoulos, PLoS ONE (2019). [doi]
4. Probabilistic forecast reconciliation with applications to wind power and electric load by Jooyoung Jeon, Anastasios Panagiotelis, Fotios Petropoulos, European Journal of Operational Research (2019). [doi] [probabilistic]
5. Temporal hierarchies with autocorrelation for load forecasting by Peter Nystrup, Erik Lindstr\om, Pierre Pinson, Henrik Madsen, European Journal of Operational Research (2020). [doi]
6. Heat load forecasting using adaptive temporal hierarchies by Hj\orleifur G. Bergsteinsson, Jan Kloppenborg M\oller, Peter Nystrup, 'Olafur Pétur Pálsson, Daniela Guericke, Henrik Madsen, Applied Energy (2021). [doi]
7. Dimensionality reduction in forecasting with temporal hierarchies by Peter Nystrup, Erik Lindstr\om, Jan K. M\oller, Henrik Madsen, International Journal of Forecasting (2021). [doi]
8. Hierarchical forecast reconciliation with machine learning by Evangelos Spiliotis, Mahdi Abolghasemi, Rob J Hyndman, Fotios Petropoulos, Vassilios Assimakopoulos, Applied Soft Computing (2021).
9. Forecasting with deep temporal hierarchies by Filotas Theodosiou, Nikolaos Kourentzes, Available at SSRN 3918315 (2021).
10. Deep Learning Temporal Hierarchies for Interval Forecasts by Filotas Theodosiou, Nikolaos Kourentzes (2021). [url] [probabilistic]
11. Temporal Hierarchical Reconciliation for Consistent Water Resources Forecasting Across Multiple Timescales: An Application to Precipitation Forecasting by M S Jahangir, J Quilty, Water resources research (2022). [doi] [url]
12. Dynamic Temporal Reconciliation by Reinforcement learning by Himanshi Charotia, Abhishek Garg, Gaurav Dhama, Naman Maheshwari (2022). [arXiv]
13. Efficient probabilistic reconciliation of forecasts for real-valued and count time series by Lorenzo Zambon, Dario Azzimonti, Giorgio Corani, Statistics and Computing (2022). [doi] [arXiv] [probabilistic]
14. Likelihood-Based Inference in Temporal Hierarchies. by Møller, Jan Kloppenborg, Peter Nystrup, and Henrik Madsen, International Journal of Forecasting (2023). [doi]
15. Reconciling Temporal Hierarchies of Wind Power Production with Forecast-Dependent Variance Structures by Sørensen, Mikkel L., Jan K. Møller, and Henrik Madsen, European Mathematical Society Magazine (2023). [doi]
16. Forecast reconciliation in the temporal hierarchy: Special case of intermittent demand with obsolescence by Kamal Sanguri, Sabyasachi Patra, Sushil Punia, Expert Systems with Applications (2023) [[doi]]](https://doi.org/10.1016/j.eswa.2023.119566)
17. Check for Updates Electricity Load and Peak Forecasting: Feature Engineering, Probabilistic Light GBM and Temporal Hierarchies by Rubattu, Nicolò, Gabriele Maroni, and Giorgio Corani, Advanced Analytics and Learning on Temporal Data: 8th ECML PKDD Workshop (2023). [probabilistic]
18. Optimal Forecast Reconciliation with Uncertainty Quantification. by Møller, Jan Kloppenborg, Peter Nystrup, Poul G. Hjorth, and Henrik Madsen (2024). [arXiv]
19. Optimal Reconciliation of Hierarchical Wind Energy Forecasts Utilizing Temporal Correlation. by Navneet Sharma, Rohit Bhakar, and Prerna Jain, Energy Conversion and Management (2024). [doi]

1. A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing by Chongshou Li, Andrew Lim, European Journal of Operational Research (2018). [doi]
2. Cross-temporal coherent forecasts for Australian tourism by Nikolaos Kourentzes, George Athanasopoulos, Annals of Tourism Research (2019). [doi]
3. Reconciling solar forecasts: Sequential reconciliation by Gokhan Mert Yagli, Dazhi Yang, Dipti Srinivasan, Solar Energy (2019). [doi]
4. A cross-temporal hierarchical framework and deep learning for supply chain forecasting by Sushil Punia, Surya P. Singh, Jitendra K. Madaan, Computers & Industrial Engineering (2020). [doi]
5. Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption by Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Kourentzes, Vassilios Assimakopoulos, Applied Energy (2020). [doi]
6. Enhancements in cross-temporal forecast reconciliation, with an application to solar irradiance forecasts by Tommaso Di Fonzo, Daniele Girolimetto (2022). [arXiv]
7. Toward a one-number forecast: cross-temporal hierarchies by Nikolaos Kourentzes, Foresight: The International Journal of Applied Forecasting (2022).
8. Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives by Tommaso Di Fonzo, Daniele Girolimetto, International Journal of Forecasting (2023). [doi] [github] [arXiv]
9. Spatio-temporal reconciliation of solar forecasts by Tommaso Di Fonzo, Daniele Girolimetto, Solar Energy (2023).
10. A novel reconciliation approach for hierarchical electricity consumption forecasting based on resistant regression by Erick Meira, Maur'\icio Franca Lila, Fernando Luiz Cyrino Oliveira, Energy (2023). [doi] [probabilistic]
11. Cross-temporal Probabilistic Forecast Reconciliation by Daniele Girolimetto, George Athanasopoulos, Tommaso Di Fonzo, Rob J Hyndman, International Journal of Forecasting (2024). [doi] [github] [arXiv] [probabilistic]

1. gtop: Game-Theoretically OPtimal (GTOP) Reconciliation Method by Jairo Cugliari, Tim van Erven (2015). [R archive]
2. thief: Temporal HIErarchical Forecasting by Rob J Hyndman, Nikolaos Kourentzes (2018). [R cran] [docu] [github]
3. hts: Hierarchical and Grouped Time Series by Rob J Hyndman, Alan Lee, Earo Wang, Shanika Wickramasuriya (2021). [R cran] [docu] [github]
4. ProbReco: Score Optimal Probabilistic Forecast Reconciliation by Anastasios Panagiotelis (2020). [R archive] [github]
5. FoReco: Forecast Reconciliation by Daniele Girolimetto, Tommaso Di Fonzo (2023). [R cran] [docu] [github]
6. bayesRecon: Probabilistic Reconciliation via Conditioning by Dario Azzimonti, Nicolò Rubattu, Lorenzo Zambon, Giorgio Corani (2023). [R cran] [github]
7. fabletools: Core Tools for Packages in the 'fable' Framework by Mitchell O'Hara-Wild, Rob J Hyndman, Earo Wang (2023). [R cran] [docu] [github]

1. pyhts: A python package for hierarchical forecasting by Bohan Zhang, Yanfei Kang, Feng Li (2022). [docu] [github]
2. GluonTS: Probabilistic Time Series Modeling in Python by A. Alexandrov, K. Benidis, M. Bohlke-Schneider, V. Flunkert, J. Gasthaus, T. Januschowski, D. C. Maddix, S. Rangapuram, D. Salinas, J. Schulz, L. Stella, A. C. Türkmen, Y. Wang (2023). [url]
   • Accompanying paper: GluonTS: Probabilistic and Neural Time Series Modeling in Python by Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang, Journal of Machine Learning Research (2020). [url]
3. HierarchicalForecast: Probabilistic hierarchical forecasting with statistical and econometric methods by Kin G. Olivares, Federico Garza, David Luo, Cristian Challú, Max Mergenthaler, Souhaib Ben Taieb, Shanika L. Wickramasuriya, Artur Dubrawski (2022). [url]
   • Accompanying paper: HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python by Kin G. Olivares, Federico Garza, David Luo, Cristian Challú, Max Mergenthaler, Souhaib Ben Taieb, Shanika L. Wickramasuriya, Artur Dubrawski (2023). [arXiv]

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37. An aggregate--disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis by Konstantinos Nikolopoulos, Aris A Syntetos, John E Boylan, Fotios Petropoulos, Vassilis Assimakopoulos, Journal of the Operational Research Society (2011).
38. Restoring accounting constraints in time series: methods and software for a statistical agency by Benoit Quenneville, Susie Fortier, Economic Time Series: Modeling and Seasonality (2012).
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40. Demand forecasting by temporal aggregation by Bahman Rostami-Tabar, M Zied Babai, Aris Syntetos, Yves Ducq, Naval Research Logistics (2013).
41. Improving forecasting by estimating time series structural components across multiple frequencies by Nikolaos Kourentzes, Fotios Petropoulos, Juan R Trapero, International Journal of Forecasting (2014).
42. A systemic view of the ADIDA framework by Georgios P Spithourakis, Fotios Petropoulos, Konstantinos Nikolopoulos, Vassilios Assimakopoulos, IMA Journal of Management Mathematics (2014).
43. A note on the forecast performance of temporal aggregation by Bahman Rostami-Tabar, Mohamed Zied Babai, Aris Syntetos, Yves Ducq, Naval Research Logistics (2014).
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1. Forecasting hierarchical time series by Roman A Ahmed, Monash PhD Thesis (2009). [ur]
2. Optimal forecasts for hierarchical and grouped time series by Shanika L Wickramasuriya, Monash PhD Thesis (2017). [url]
3. Essays in hierarchical time series forecasting and forecast combination by Christoph Weiss, University of Cambridge PhD Thesis (2018). [doi]
4. Probabilistic Forecast Reconciliation: Theory and Applications by Puwasala Gamakumara, Monash PhD Thesis (2020). [url]
5. Forecast reconciliation: Methodological issues and applications by Daniele Girolimetto, UniPD PhD Thesis (2020). [url]