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Optimal combination forecasts for hierarchical time series

Citations

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Cited by:

  1. Moon, Seongmin & Simpson, Andrew & Hicks, Christian, 2013. "The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand," International Journal of Production Economics, Elsevier, vol. 143(2), pages 449-454.
  2. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
  3. Eckert, Florian & Hyndman, Rob J. & Panagiotelis, Anastasios, 2021. "Forecasting Swiss exports using Bayesian forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 693-710.
  4. Wang, Xiaoqian & Hyndman, Rob J. & Wickramasuriya, Shanika L., 2025. "Optimal forecast reconciliation with time series selection," European Journal of Operational Research, Elsevier, vol. 323(2), pages 455-470.
  5. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
  6. Abolghasemi, Mahdi & Tarr, Garth & Bergmeir, Christoph, 2024. "Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 597-615.
  7. Calderón-Villarreal, Cuauhtémoc & Hernández-Bielma, Leticia, 2016. "Cambio estructural y desindustrialización en México./ Structural Change and desindustrialisation in Mexico," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 12(23), pages 29-54, Segundo s.
  8. Cengiz, Doruk & Tekgüç, Hasan, 2024. "Counterfactual reconciliation: Incorporating aggregation constraints for more accurate causal effect estimates," International Journal of Forecasting, Elsevier, vol. 40(2), pages 564-580.
  9. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
  10. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
  11. Daniel Kosiorowski & Dominik Mielczarek & Jerzy P. Rydlewski, 2018. "Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for the Day and Night Air Pollution in Silesia Region - A Critical Overview," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 53-73, March.
  12. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
  13. Yang, Yang & Shang, Han Lin & Raymer, James, 2024. "Forecasting Australian fertility by age, region, and birthplace," International Journal of Forecasting, Elsevier, vol. 40(2), pages 532-548.
  14. Fernando, Angeline Gautami & Aw, Eugene Cheng-Xi, 2023. "What do consumers want? A methodological framework to identify determinant product attributes from consumers’ online questions," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
  15. Sbrana, Giacomo & Pelagatti, Matteo, 2024. "Optimal hierarchical EWMA forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 616-625.
  16. Girolimetto, Daniele & Athanasopoulos, George & Di Fonzo, Tommaso & Hyndman, Rob J., 2024. "Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1134-1151.
  17. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
  18. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
  19. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, vol. 27(3), pages 666-677, May.
  20. Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
  21. In, YeonJun & Jung, Jae-Yoon, 2022. "Simple averaging of direct and recursive forecasts via partial pooling using machine learning," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1386-1399.
  22. Zeynep Hilal Kilimci & A. Okay Akyuz & Mitat Uysal & Selim Akyokus & M. Ozan Uysal & Berna Atak Bulbul & Mehmet Ali Ekmis, 2019. "An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain," Complexity, Hindawi, vol. 2019, pages 1-15, March.
  23. Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "Evaluating quantile forecasts in the M5 uncertainty competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1531-1545.
  24. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
  25. Olivares, Kin G. & Meetei, O. Nganba & Ma, Ruijun & Reddy, Rohan & Cao, Mengfei & Dicker, Lee, 2024. "Probabilistic hierarchical forecasting with deep Poisson mixtures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 470-489.
  26. Wilson, Tom & Grossman, Irina & Temple, Jeromey, 2023. "Evaluation of the best M4 competition methods for small area population forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 110-122.
  27. Ma, Shaohui & Fildes, Robert, 2022. "The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1492-1499.
  28. Panagiotelis, Anastasios & Gamakumara, Puwasala & Athanasopoulos, George & Hyndman, Rob J., 2023. "Probabilistic forecast reconciliation: Properties, evaluation and score optimisation," European Journal of Operational Research, Elsevier, vol. 306(2), pages 693-706.
  29. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
  30. Zambon, Lorenzo & Agosto, Arianna & Giudici, Paolo & Corani, Giorgio, 2024. "Properties of the reconciled distributions for Gaussian and count forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1438-1448.
  31. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2021. "Stochastic coherency in forecast reconciliation," International Journal of Production Economics, Elsevier, vol. 240(C).
  32. Shanika L Wickramasuriya & George Athanasopoulos & Rob J Hyndman, 2015. "Forecasting hierarchical and grouped time series through trace minimization," Monash Econometrics and Business Statistics Working Papers 15/15, Monash University, Department of Econometrics and Business Statistics.
  33. Han Lin Shang, 2017. "Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 55-84, February.
  34. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
  35. Kourentzes, Nikolaos & Athanasopoulos, George, 2019. "Cross-temporal coherent forecasts for Australian tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 393-409.
  36. Han Lin Shang & Rob J Hyndman, 2016. "Grouped functional time series forecasting: An application to age-specific mortality rates," Monash Econometrics and Business Statistics Working Papers 4/16, Monash University, Department of Econometrics and Business Statistics.
  37. Jeon, Jooyoung & Panagiotelis, Anastasios & Petropoulos, Fotios, 2019. "Probabilistic forecast reconciliation with applications to wind power and electric load," European Journal of Operational Research, Elsevier, vol. 279(2), pages 364-379.
  38. Xiaodan Zhu & Anh Ninh & Hui Zhao & Zhenming Liu, 2021. "Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3231-3252, September.
  39. Sprangers, Olivier & Wadman, Wander & Schelter, Sebastian & de Rijke, Maarten, 2024. "Hierarchical forecasting at scale," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1689-1700.
  40. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
  41. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
  42. Daniele Girolimetto & Tommaso Di Fonzo, 2024. "Point and probabilistic forecast reconciliation for general linearly constrained multiple time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 581-607, April.
  43. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
  44. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
  45. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
  46. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
  47. Capistrán Carlos & Constandse Christian & Ramos Francia Manuel, 2009. "Using Seasonal Models to Forecast Short-Run Inflation in Mexico," Working Papers 2009-05, Banco de México.
  48. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
  49. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
  50. Barkha Parkash & Tek Tjing Lie & Weihua Li & Shafiqur Rahman Tito, 2024. "End-to-End Top-Down Load Forecasting Model for Residential Consumers," Energies, MDPI, vol. 17(11), pages 1-20, May.
  51. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 16-32.
  52. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
  53. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
  54. Sonaxy Mohanty & Airi Shimamura & Charles D. Nicholson & Andrés D. González & Talayeh Razzaghi, 2025. "Hierarchical Time Series Forecasting of COVID-19 Cases Using County-Level Clustering Data," SN Operations Research Forum, Springer, vol. 6(1), pages 1-28, March.
  55. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
  56. Souhaib Ben Taieb & James W. Taylor & Rob J. Hyndman, 2017. "Coherent Probabilistic Forecasts for Hierarchical Time Series," Monash Econometrics and Business Statistics Working Papers 3/17, Monash University, Department of Econometrics and Business Statistics.
  57. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
  58. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2019. "Improving the forecasting performance of temporal hierarchies," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-21, October.
  59. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  60. Di Fonzo, Tommaso & Girolimetto, Daniele, 2024. "Forecast combination-based forecast reconciliation: Insights and extensions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 490-514.
  61. Li Bai & Pierre Pinson, 2019. "Distributed Reconciliation in Day-Ahead Wind Power Forecasting," Energies, MDPI, vol. 12(6), pages 1-19, March.
  62. Sali, Mustapha & Ghrab, Yahya & Chatras, Clément, 2023. "Optimal product aggregation for sales and operations planning in mass customisation context," International Journal of Production Economics, Elsevier, vol. 263(C).
  63. Abolghasemi, Mahdi & Girolimetto, Daniele & Di Fonzo, Tommaso, 2025. "Improving cross-temporal forecasts reconciliation accuracy and utility in energy market," Applied Energy, Elsevier, vol. 394(C).
  64. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
  65. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
  66. Kafa, Nadine & Babai, M. Zied & Klibi, Walid, 2025. "Forecasting mail flow: A hierarchical approach for enhanced societal wellbeing," International Journal of Forecasting, Elsevier, vol. 41(1), pages 51-65.
  67. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2018. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," MPRA Paper 91762, University Library of Munich, Germany.
  68. Corani, Giorgio & Azzimonti, Dario & Rubattu, Nicolò, 2024. "Probabilistic reconciliation of count time series," International Journal of Forecasting, Elsevier, vol. 40(2), pages 457-469.
  69. Zhiwei Qin & John Bowman & Jagtej Bewli, 2018. "A Bayesian framework for large-scale geo-demand estimation in on-line retailing," Annals of Operations Research, Springer, vol. 263(1), pages 231-245, April.
  70. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
  71. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
  72. Rania A. H. Mohamed, 2023. "Enhancing forecast accuracy using combination methods for the hierarchical time series approach," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-18, July.
  73. Roach, Cameron, 2019. "Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1439-1450.
  74. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
  75. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
  76. Shanika L. Wickramasuriya & George Athanasopoulos & Rob J. Hyndman, 2019. "Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 804-819, April.
  77. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
  78. Rombouts, Jeroen & Ternes, Marie & Wilms, Ines, 2025. "Cross-temporal forecast reconciliation at digital platforms with machine learning," International Journal of Forecasting, Elsevier, vol. 41(1), pages 321-344.
  79. Olukunle O. Owolabi & Deborah A. Sunter, 2022. "Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages," Energies, MDPI, vol. 15(6), pages 1-22, March.
  80. Tomokaze Shiratori & Ken Kobayashi & Yuichi Takano, 2020. "Prediction of hierarchical time series using structured regularization and its application to artificial neural networks," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
  81. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
  82. Long, Xueying & Bui, Quang & Oktavian, Grady & Schmidt, Daniel F. & Bergmeir, Christoph & Godahewa, Rakshitha & Lee, Seong Per & Zhao, Kaifeng & Condylis, Paul, 2025. "Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach," International Journal of Production Economics, Elsevier, vol. 279(C).
  83. Lila, Maurício Franca & Meira, Erick & Cyrino Oliveira, Fernando Luiz, 2022. "Forecasting unemployment in Brazil: A robust reconciliation approach using hierarchical data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
  84. Mahsa Ashouri & Rob J Hyndman & Galit Shmueli, 2019. "Fast Forecast Reconciliation Using Linear Models," Monash Econometrics and Business Statistics Working Papers 29/19, Monash University, Department of Econometrics and Business Statistics.
  85. Puwasala Gamakumara & Anastasios Panagiotelis & George Athanasopoulos & Rob J Hyndman, 2018. "Probabilisitic forecasts in hierarchical time series," Monash Econometrics and Business Statistics Working Papers 11/18, Monash University, Department of Econometrics and Business Statistics.
  86. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
  87. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
  88. Colin O. Quinn & George F. Corliss & Richard J. Povinelli, 2024. "Cross-Temporal Hierarchical Forecast Reconciliation of Natural Gas Demand," Energies, MDPI, vol. 17(13), pages 1-18, June.
  89. Zhang, Bohan & Panagiotelis, Anastasios & Li, Han, 2025. "Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1022-1036.
  90. Robinson Kruse‐Becher, 2025. "Adaptive Now‐ and Forecasting of Global Temperatures Under Smooth Structural Changes," Environmetrics, John Wiley & Sons, Ltd., vol. 36(6), September.
  91. Abouarghoub, Wessam & Nomikos, Nikos K. & Petropoulos, Fotios, 2018. "On reconciling macro and micro energy transport forecasts for strategic decision making in the tanker industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 113(C), pages 225-238.
  92. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
  93. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
  94. Mostafa Rezaei & Armann Ingolfsson, 2024. "Forecasting to support EMS tactical planning: what is important and what is not," Health Care Management Science, Springer, vol. 27(4), pages 604-630, December.
  95. Mustapha Sali & Yahya Ghrab & Clément Chatras, 2023. "Optimal product aggregation for sales and operations planning in mass customisation context," Post-Print hal-04319422, HAL.
  96. Spyros Makridakis & Chris Fry & Fotios Petropoulos & Evangelos Spiliotis, 2022. "The Future of Forecasting Competitions: Design Attributes and Principles," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 96-113, April.
  97. Tucker McElroy, 2018. "Seasonal adjustment subject to accounting constraints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 574-589, November.
  98. Marinoiu Cristian, 2016. "Forecasting The Number Of Unemployed People From Romania Using Hierarchical Time Series," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 91-97, August.
  99. Bergsteinsson, Hjörleifur G. & Møller, Jan Kloppenborg & Nystrup, Peter & Pálsson, Ólafur Pétur & Guericke, Daniela & Madsen, Henrik, 2021. "Heat load forecasting using adaptive temporal hierarchies," Applied Energy, Elsevier, vol. 292(C).
  100. Petropoulos, Fotios & Spiliotis, Evangelos, 2025. "Judgmental selection of parameters for simple forecasting models," European Journal of Operational Research, Elsevier, vol. 323(3), pages 780-794.
  101. Brégère, Margaux & Huard, Malo, 2022. "Online hierarchical forecasting for power consumption data," International Journal of Forecasting, Elsevier, vol. 38(1), pages 339-351.
  102. Cengiz, Doruk & Tekgüç, Hasan, 2022. "Counterfactual Reconciliation: Incorporating Aggregation Constraints For More Accurate Causal Effect Estimates," MPRA Paper 114478, University Library of Munich, Germany.
  103. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
  104. Li, Han & Li, Hong & Lu, Yang & Panagiotelis, Anastasios, 2019. "A forecast reconciliation approach to cause-of-death mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 122-133.
  105. Daniel Kosiorowski & Dominik Mielczarek & Jerzy. P. Rydlewski, 2017. "Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview," Papers 1712.03797, arXiv.org.
  106. Panagiotelis, Anastasios & Athanasopoulos, George & Gamakumara, Puwasala & Hyndman, Rob J., 2021. "Forecast reconciliation: A geometric view with new insights on bias correction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 343-359.
  107. da Silva, Felipe L.C. & Cyrino Oliveira, Fernando L. & Souza, Reinaldo C., 2019. "A bottom-up bayesian extension for long term electricity consumption forecasting," Energy, Elsevier, vol. 167(C), pages 198-210.
  108. Ana Caroline Pinheiro & Paulo Canas Rodrigues, 2024. "Hierarchical Time Series Forecasting of Fire Spots in Brazil: A Comprehensive Approach," Stats, MDPI, vol. 7(3), pages 1-24, June.
  109. Zhang, Bohan & Panagiotelis, Anastasios & Kang, Yanfei, 2024. "Discrete forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 318(1), pages 143-153.
  110. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
  111. Pietro Giorgio Lovaglio, 2025. "Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 753-780, March.
  112. Li, Han & Chen, Hua, 2024. "Hierarchical mortality forecasting with EVT tails: An application to solvency capital requirement," International Journal of Forecasting, Elsevier, vol. 40(2), pages 549-563.
  113. Mamonov, Nikolay & Golubyatnikov, Evgeny & Kanevskiy, Daniel & Gusakov, Igor, 2022. "GoodsForecast second-place solution in M5 Uncertainty track: Combining heterogeneous models for a quantile estimation task," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1434-1441.
  114. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
  115. Tang, Wenjin & Bu, Hui & Ji, Yuqiong & Li, Zhongfei, 2024. "Market uncertainty and information content in complex seasonality of prices," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
  116. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
  117. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
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