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Understanding forecast reconciliation

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Cited by:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Corani, Giorgio & Azzimonti, Dario & Rubattu, Nicolò, 2024. "Probabilistic reconciliation of count time series," International Journal of Forecasting, Elsevier, vol. 40(2), pages 457-469.
  9. 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.
  10. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).
  11. 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.
  12. Zhang, Bohan & Panagiotelis, Anastasios & Kang, Yanfei, 2024. "Discrete forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 318(1), pages 143-153.
  13. 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.
  14. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
  15. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.
  16. Meira, Erick & Lila, Maurício Franca & Cyrino Oliveira, Fernando Luiz, 2023. "A novel reconciliation approach for hierarchical electricity consumption forecasting based on resistant regression," Energy, Elsevier, vol. 269(C).
  17. Mikkel L. Sørensen & Peter Nystrup & Mathias B. Bjerregård & Jan K. Møller & Peder Bacher & Henrik Madsen, 2023. "Recent developments in multivariate wind and solar power forecasting," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
  18. 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.
  19. Ghelasi, Paul & Ziel, Florian, 2024. "Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 581-596.
  20. 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).
  21. 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.
  22. Redouane Benabdallah Benarmas & Kadda Beghdad Bey, 2024. "A deep learning hierarchical approach to road traffic forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1294-1307, August.
  23. Kohút, Roman & Klaučo, Martin & Kvasnica, Michal, 2025. "Unified carbon emissions and market prices forecasts of the power grid," Applied Energy, Elsevier, vol. 377(PC).
  24. 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).
  25. Cengiz, Doruk & Tekgüç, Hasan, 2022. "Counterfactual Reconciliation: Incorporating Aggregation Constraints For More Accurate Causal Effect Estimates," MPRA Paper 114478, University Library of Munich, Germany.
  26. 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.
  27. Møller, Jan Kloppenborg & Nystrup, Peter & Madsen, Henrik, 2024. "Likelihood-based inference in temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 40(2), pages 515-531.
  28. Li, Xinyi & Wang, Shitong & Chen, Zhiqiang, 2024. "Hierarchical reconciliation of convolutional gated recurrent units for unified forecasting of branched and aggregated district heating loads," Energy, Elsevier, vol. 313(C).
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