Awesome Forecast Reconciliation

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 list is still incomplete and unorganized. We are in the process of reorganizing the various items.

Unsorted list

1. Machine learning applications in time series hierarchical forecasting by Mahdi Abolghasemi, Rob J Hyndman, Garth Tarr, Christoph Bergmeir (2019). [arXiv]

2. 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).

3. Model selection in reconciling hierarchical time series by Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis, Christoph Bergmeir, Machine Learning (2022). [DOI]

4. Forecasting hierarchical time series by Roman A Ahmed (2009).

5. Hierarchical Time Series Forecast in Electrical Grids by Vȧnia Almeida, Rita Ribeiro, Joȧo Gama, Information Science and Applications (ICISA) (2016). [DOI]

6. The Effect of Aggregation on Prediction in the Autoregressive model by Takeshi Amemiya, Roland Y. Wu, Journal of the American Statistical Association (1972). [DOI]

7. Hierarchical forecasting with a top-down alignment of independent level forecasts by Matthias Anderer, Feng Li, International Journal of Forecasting (2022). [DOI]

8. Fast forecast reconciliation using linear models by Mahsa Ashouri, Rob J. Hyndman, Galit Shmueli, Journal of Computational & Graphical Statistics (2022). [DOI]

9. Hierarchical forecasts for Australian domestic tourism by George Athanasopoulos, Roman A Ahmed, Rob J Hyndman, International Journal of Forecasting (2009). [DOI]

10. Forecasting with temporal hierarchies by George Athanasopoulos, Rob J. Hyndman, Nikolaos Kourentzes, Fotios Petropoulos, European Journal of Operational Research (2017). [DOI]

11. Hierarchical forecasting by George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J Hyndman, Mohamed Affan, Macroeconomic Forecasting in the Era of Big Data (2020). [DOI]

12. 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).

13. On the evaluation of hierarchical forecasts by G. Athanasopoulos, N. Kourentzes, International Journal of Forecasting (2023).

14. Demand forecasting in supply chains: a review of aggregation and hierarchical approaches by M. Zied Babai, John E. Boylan, Bahman Rostami-Tabar, International Journal of Production Research (2022). [DOI]

15. Distributed Reconciliation in Day-Ahead Wind Power Forecasting by Li Bai, Pierre Pinson, Energies (2019). [DOI][URL]

16. Distributions of forecasting errors of forecast combinations: implications for inventory management by Devon K Barrow, Nikolaos Kourentzes, International Journal of Production Economics (2016).

17. 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]

18. 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]

19. 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]

20. 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]

21. 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]

22. Benchmarking large accounting frameworks: a generalized multivariate model by Reinier Bikker, Jacco Daalmans, Nino Mushkudiani, Economic Systems Research (2013).

23. Fully reconciled GDP forecasts from income and expenditure sides by Luisa Bisaglia, Tommaso Di Fonzo, Daniele Girolimetto, Book of Short Papers SIS 2020 (2020).

24. On the performance of overlapping and non-overlapping temporal demand aggregation approaches by John E Boylan, M Zied Babai, International Journal of Production Economics (2016).

25. Some consequences of temporal aggregation and systematic sampling for ARMAand ARMAX models by K.R.W. Brewer, Journal of Econometrics (1973). [DOI]

26. Online hierarchical forecasting for power consumption data by Margaux Brégėre, Malo Huard, International Journal of Forecasting (2022). [DOI]

27. A trainable reconciliation method for hierarchical time-series by Davide Burba, Trista Chen (2021). [arXiv]

28. 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]

29. The estimation of large social account matrices by R P Byron, Journal of the Royal Statistical Society, Series A (1978). [DOI][URL]

30. Corrigenda: The estimation of large social account matrices by R P Byron, Journal of the Royal Statistical Society. Series A (1979). [DOI][URL]

31. Multi-horizon inflation forecasts using disaggregated data by Carlos Capistr\a'an, Christian Constandse, Manuel Ramos-Francia, Economic Modelling (2010). [DOI]

32. Using big data to enhance demand-driven forecasting and planning by Charles W. Chase, Journal of Business Forecasting (2013).

33. Dynamic Temporal Reconciliation by Reinforcement learning by Himanshi Charotia, Abhishek Garg, Gaurav Dhama, Naman Maheshwari (2022). [arXiv]

34. Prior information and ARIMA forecasting by Pierre A Cholette, Journal of Forecasting (1982).

35. Best linear unbiased interpolation, distribution, and extrapolation of time series by related series by Gregory C Chow, An-loh Lin, The Review of Economics and Statistics (1971).

36. The forecast combination puzzle: A simple theoretical explanation by Gerda Claeskens, Jan R Magnus, Andrey L Vasnev, Wendun Wang, International Journal of Forecasting (2016).

37. Predicting Earnings with Sub-Entity Data: Some Further Evidence by Daniel W. Collins, Journal of Accounting Research (1976). [DOI]

38. 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]

39. Probabilistic reconciliation of count time series by Giorgio Corani, Nicolo Rubattu, Dario Azzimonti, Alessandro Antonucci, International Journal of Forecasting (2023). [DOI][arXiv]

40. Optimal Reconciliation of Seasonally Adjusted Disaggregates Taking Into Account the Difference Between Direct and Indirect Adjustment of the Aggregate by Francisco Corona, Victor M Guerrero, Jes'us L'opez-Per'ez, Journal of Official Statistics (2021).

41. Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series by Estela Bee Dagum, Pierre A Cholette (2006). [DOI][URL]

42. 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]

43. Top-down or bottom-up: aggregate versus disaggregate extrapolations by B J Dangerfield, J S Morris, International Journal of Forecasting (1992). [DOI]

44. 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]

45. Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting by Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen (2023). [DOI][arXiv]

46. A survey of direct methods for sparse linear systems by Timothy A. Davis, Sivasankaran Rajamanickam, Wissam M. Sid-Lakhdar, Acta Numerica (2016).

47. Constrained forecasting in autoregressive time series models: A Bayesian analysis by Enrique De Alba, International Journal of Forecasting (1993).

48. Fully reconciled probabilistic GDP forecasts from income and expenditure sides by Tommaso Di Fonzo, Daniele Girolimetto, Book of Short Papers SIS 2022 (2022).

49. Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives by Tommaso Di Fonzo, Daniele Girolimetto, International Journal of Forecasting (2023). [DOI]

50. Forecast combination-based forecast reconciliation: Insights and extensions by Tommaso Di Fonzo, Daniele Girolimetto, International Journal of Forecasting (2022). [DOI]

51. The estimation of $M$ disaggregate time series when contemporaneous and temporal aggregates are known by Tommaso Di Fonzo, The Review of Economics and Statistics (1990). [DOI][URL]

52. Enhancements in cross-temporal forecast reconciliation, with an application to solar irradiance forecasts by Tommaso Di Fonzo, Daniele Girolimetto (2022). [arXiv]

53. Simultaneous and two-step reconciliation of systems of time series: methodological and practical issues by Tommaso Di Fonzo, Marco Marini, Journal of the Royal Statistical Society, Series C (2011).

54. Spatio-temporal reconciliation of solar forecasts by Tommaso Di Fonzo, Daniele Girolimetto, Solar Energy (2023).

55. Aggregate versus subaggregate models in local area forecasting by D. M. Dunn, W. H. Williams, T. L. Dechaine, Journal of the American Statistical Association (1976). [DOI]

56. Forecasting Swiss exports using Bayesian forecast reconciliation by Florian Eckert, Rob J. Hyndman, Anastasios Panagiotelis, European Journal of Operational Research (2021). [DOI]

57. Should aggregation prior to estimation be the rule? by John B. Edwards, Guy H. Orcutt, The Review of Economics and Statistics (1969).

58. 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]

59. Comments on 'Time‐series analysis, forecasting and econometric modelling: The structural econometric modelling, time‐series analysis (SEMTSA) approach' by A. Zellner by A Espasa, Journal of Forecasting (1994). [DOI]

60. Assessment of aggregation strategies for machine-learning based short-term load forecasting by Cong Feng, Jie Zhang, Electric Power Systems Research (2020). [DOI]

61. Retail forecasting: Research and practice by Robert Fildes, Shaohui Ma, Stephan Kolassa, International Journal of Forecasting (2022). [DOI]

62. 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]

63. An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation by G Fliedner, Computers and Operations Research (1999).

64. Hierarchical forecasting: issues and use guidelines by Gene Fliedner, Industrial Management & Data Systems (2001). [DOI]

65. Constrained Forecasting: Some Implementation Guidelines by Eugene B Fliedner, Vincent A Mabert, Decision Sciences (1992).

66. Constrained Forecasting: some implementation guidelines by E B Fliedner, V A Mabert, Decision Sciences (1992).

67. LSMR: An iterative algorithm for sparse least-squares problems by David Chin Lung Fong, Michael Saunders, SIAM Journal on Scientific Computing (2011).

68. Forecasting: Principles and Practice by Rob J Hyndman, George Athanasopoulos (2018).

69. Forecasting: Principles and Practice by Rob J Hyndman, George Athanasopoulos (2021).

70. Global energy forecasting competition 2012 by Tao Hong, Pierre Pinson, Shu Fan, International Journal of Forecasting (2014). [DOI][URL]

71. Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting by Tao Hong, Jingrui Xie, Jonathan Black, International Journal of Forecasting (2019). [DOI][URL]

72. 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]

73. 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]

74. Point and probabilistic forecast reconciliation for general linearly constrained multiple time series by Daniele Girolimetto, Tommaso Di Fonzo, https://arxiv.org/abs/2305.05330 (2023).

75. Cross-temporal Probabilistic Forecast Reconciliation by Daniele Girolimetto, George Athanasopoulos, Tommaso Di Fonzo, Rob J Hyndman, International Journal of Forecasting (2023).

76. 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]

77. 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]

78. 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]

79. Disaggregation methods to expedite product line forecasting by C W Gross, J E Sohl, Journal of Forecasting (1990). [DOI]

80. Is aggregation necessarily bad? by Yehuda Grunfeld, Zvi Griliches, The Review of Economics and Statistics (1960).

81. Optimal conditional ARIMA forecasts by V'\ictor M Guerrero, Journal of forecasting (1989).

82. 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]

83. Reconciliation of wind power forecasts in spatial hierarchies by Mads E Hansen, Nystrup Peter, Jan K M\oller, Madsen Henrik, Wind Energy (2023).

84. Highest-density forecast regions for nonlinear and non-normal time series models by Rob J Hyndman, J Forecasting (1995).

85. Neural networks for short-term load forecasting: A review and evaluation by Henrique Steinherz Hippert, Carlos Eduardo Pedreira, Reinaldo Castro Souza, IEEE Transactions on Power Systems (2001).

86. Understanding forecast reconciliation by Ross Hollyman, Fotios Petropoulos, Michael E. Tipping, European Journal of Operational Research (2021). [DOI]

87. The effect of aggregation on prediction in autoregressive integrated moving-average models by L. K. Hotta, J. Cardoso Neto, Journal of Time Series Analysis (1993).

88. The effect of overlapping aggregation on time series models: an application to the unemployment rate in Brazil by Luiz K Hotta, Pedro A Morettin, Pedro L Valls Pereira, Brazilian Review of Econometrics (1992).

89. Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy? by K Hubrich, International Journal of Forecasting (2005).

90. Improving forecasts for noisy geographic time series by S H Huddlestone, J H Porter, D E Brown, Journal of Business Research (2015).

91. Optimally reconciling forecasts in a hierarchy by R J Hyndman, George Athanasopoulos, Foresight: International Journal of Applied Forecasting (2014). [URL]

92. Forecasting: Principles and Practice by Rob J. Hyndman, George Athanasopoulos (2021). [URL]

93. 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]

94. 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]

95. Aggregation vs disaggregation in forecasting construction activities by Pekka Ilmakunnas, Disaggregation in econometric modelling (1990).

96. 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]

97. 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]

98. Revisiting top-down versus bottom-up forecasting by Kenneth B Kahn, The Journal of Business Forecasting Methods & Systems (1998).

99. 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]

100. 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]

101. Predicting earnings: entity versus subentity data by William R Kinney Jr., Journal of Accounting Research (1971).

102. Do we want coherent hierarchical forecasts, or minimal MAPEs or MAEs? (We won't get both!) by Stephan Kolassa, International Journal of Forecasting (2023).

103. Evaluating predictive count data distributions in retail sales forecasting by Stephan Kolassa, International Journal of Forecasting (2016).

104. 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]

105. 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]

106. 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]

107. 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]

108. Cross-temporal coherent forecasts for Australian tourism by Nikolaos Kourentzes, George Athanasopoulos, Annals of Tourism Research (2019). [DOI]

109. Elucidate structure in intermittent demand series by Nikolaos Kourentzes, George Athanasopoulos, European Journal of Operational Research (2021). [DOI]

110. Improving forecasting by estimating time series structural components across multiple frequencies by Nikolaos Kourentzes, Fotios Petropoulos, Juan R Trapero, International Journal of Forecasting (2014).

111. On intermittent demand model optimisation and selection by Nikolaos Kourentzes, International Journal of Production Economics (2014).

112. Forecasting with multivariate temporal aggregation: The case of promotional modelling by Nikolaos Kourentzes, Fotios Petropoulos, International Journal of Production Economics (2016).

113. Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels? by Nikolaos Kourentzes, Bahman Rostami-Tabar, Devon K Barrow, Journal of Business Research (2017).

114. 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).

115. Toward a one-number forecast: cross-temporal hierarchies by Nikolaos Kourentzes, Foresight: The International Journal of Applied Forecasting (2022).

116. The sum and its parts: judgmental hierarchical forecasting by Mirko Kremer, Enno Siemsen, Douglas J Thomas, Management Science (2016). [DOI]

117. High-dimensional covariance matrix estimation by Clifford Lam, Wiley Interdisciplinary reviews: computational statistics (2020).

118. A simple view of top-down vs bottom-up forecasting by L Lapide, Journal of Business Forecasting Methods and Systems (1998).

119. A forecast reconciliation approach to cause-of-death mortality modeling by Han Li, Hong Li, Yang Lu, Anastasios Panagiotelis, Insurance: Mathematics and Economics (2019).

120. 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]

121. 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][URL]

122. A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing by Chongshou Li, Andrew Lim, European Journal of Operational Research (2018). [DOI]

123. Analyzing mortality bond indexes via hierarchical forecast reconciliation by Han Li, Qihe Tang, ASTIN Bulletin (2019). [DOI]

124. 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]

125. Forecasting contemporaneously aggregated vector by H L\utkepohl, Journal of Business & Economic Statistics (1984). [DOI]

126. Linear transformations of vector ARMA processes by H L\utkepohl, Journal of Econometrics (1984).

127. Forecasting temporally aggregated vector ARMA processes by Helmut L\utkepohl, Journal of Forecasting (1986). [URL]

128. The M5 accuracy competition: Results, findings and conclusions by Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos (2022). [URL]

129. A machine learning approach for forecasting hierarchical time series by Paolo Mancuso, Veronica Piccialli, Antonio M. Sudoso, Expert Systems with Applications (2021). [DOI]

130. 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).

131. 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]

132. A self-supervised approach to hierarchical forecasting with applications to groupwise synthetic controls by Konstantin Mishchenko, Mallory Montgomery, Federico Vaggi (2019). [arXiv]

133. Forecast horizon aggregation in integer autoregressive moving average (INARMA) models by Maryam Mohammadipour, John E Boylan, Omega (2012).

134. 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).

135. 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).

136. Hierarchical demand forecasting benchmark for the distribution grid by Lorenzo Nespoli, Vasco Medici, Kristijan Lopatichki, Fabrizio Sossan, Electric Power Systems Research (2020). [DOI]

137. Predictive accuracy gain from disaggregate sampling in ARIMA models by Theo E Nijman, Franz C Palm, Journal of Business & Economic Statistics (1990). [DOI]

138. 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).

139. Notation for forecast reconciliation by Rob J Hyndman (2022). [URL]

140. A Bayesian model for forecasting hierarchically structured time series by Julie Novak, Scott McGarvie, Beatriz Etchegaray Garcia (2017). [arXiv]

141. Temporal hierarchies with autocorrelation for load forecasting by Peter Nystrup, Erik Lindstr\om, Pierre Pinson, Henrik Madsen, European Journal of Operational Research (2020). [DOI]

142. 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]

143. Probabilistic hierarchical forecasting with deep Poisson mixtures by Kin G Olivares, O Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, Lee Dicker (2022). [arXiv]

144. Assessing the performance of hierarchical forecasting methods on the retail sector by José Manuel Oliveira, Patrícia Ramos, Entropy (2019). [DOI]

145. 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]

146. Data aggregation and information loss by Guy H. Orcutt, Harold W. Watts, John B. Edwards, The American Economic Review (1968).

147. Algorithm 583: LSQR: Sparse linear equations and least squares problems by Christopher C Paige, Michael A Saunders, ACM Transactions on Mathematical Software (TOMS) (1982).

148. 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]

149. 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]

150. Coherent probabilistic solar power forecasting by Hossein Panamtash, Qun Zhou, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (2018). [DOI]

151. Hierarchically regularized deep forecasting by Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das (2021). [arXiv]

152. 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]

153. Integrated hierarchical forecasting by Clint L.P. Pennings, Jan van Dalen, European Journal of Operational Research (2017). [DOI]

154. Forecast combinations for intermittent demand by Fotios Petropoulos, Nikolaos Kourentzes, Journal of the Operational Research Society (2015).

155. Another look at estimators for intermittent demand by Fotios Petropoulos, Nikolaos Kourentzes, Konstantinos Nikolopoulos, International Journal of Production Economics (2016).

156. The inventory performance of forecasting methods: Evidence from the M3 competition data by Fotios Petropoulos, Xun Wang, Stephen M Disney, International Journal of Forecasting (2019).

157. Modelling and forecasting linear combinations of time series by Francisco A Pino, Pedro A Morettin, Ra'ul P Mentz, International Statistical Review/Revue Internationale de Statistique (1987).

158. Stochastic coherency in forecast reconciliation by Kandrika F. Pritularga, Ivan Svetunkov, Nikolaos Kourentzes, International Journal of Production Economics (2021). [DOI]

159. Stochastic coherency in forecast reconciliation by Kandrika F Pritularga, Ivan Svetunkov, Nikolaos Kourentzes, International Journal of Production Economics (2021).

160. 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]

161. Darts: Time Series Made Easy in Python by Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Gaël Grosch (2023). [URL]

162. Darts: User-Friendly Modern Machine Learning for Time Series by Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Gaël Grosch, Journal of Machine Learning Research (2022). [URL]

163. 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]

164. A python package for hierarchical forecasting by Bohan Zhang, Yanfei Kang, Feng Li (2022). [URL]

165. Hierarchical accounting variables forecasting by deep learning methods by Mengke Qiao, Ke-Wei Huang, ICIS 2018 Proceedings 7 (2018). [URL]

166. 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). [URL]

167. 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]

168. bayesRecon: Probabilistic Reconciliation via Conditioning by Dario Azzimonti, Nicolò Rubattu, Lorenzo Zambon, Giorgio Corani (2023). [URL]

169. 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]

170. fabletools: Core Tools for Packages in the 'fable' Framework by Mitchell O'Hara-Wild, Rob J Hyndman, Earo Wang (2023). [URL]

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