Citations (44)
Keep up to date with the latest research on this topic with citation updates for this article.
Read on this site (9)
Dejan Mircetic, Bahman Rostami-Tabar, Svetlana Nikolicic & Marinko Maslaric. (2022) Forecasting hierarchical time series in supply chains: an empirical investigation. International Journal of Production Research 60:8, pages 2514-2533.
Read now
Read now
M. Zied Babai, John E. Boylan & Bahman Rostami-Tabar. (2022) Demand forecasting in supply chains: a review of aggregation and hierarchical approaches. International Journal of Production Research 60:1, pages 324-348.
Read now
Read now
Hakeem-Ur Rehman, Guohua Wan, Azmat Ullah & Badiea Shaukat. (2019) Individual and combination approaches to forecasting hierarchical time series with correlated data: an empirical study. Journal of Management Analytics 6:3, pages 231-249.
Read now
Read now
Teun van Gils, Katrien Ramaekers, An Caris & Mario Cools. (2017) The use of time series forecasting in zone order picking systems to predict order pickers’ workload. International Journal of Production Research 55:21, pages 6380-6393.
Read now
Read now
Gu Pang & Bartosz Gebka. (2017) Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia. International Journal of Production Research 55:9, pages 2454-2469.
Read now
Read now
Han Lin Shang & Rob J. Hyndman. (2017) Grouped Functional Time Series Forecasting: An Application to Age-Specific Mortality Rates. Journal of Computational and Graphical Statistics 26:2, pages 330-343.
Read now
Read now
Muhammad Akram, Ishaq Bhatti, Muhammad Ashfaq & Asif Ali Khan. (2017) Hierarchical Forecasts of Agronomy-Based Data. American Journal of Mathematical and Management Sciences 36:1, pages 49-65.
Read now
Read now
Argon Chen, Kyle Yang & Ziv Hsia. (2008) Weighted least-square estimation of demand product mix and its applications to semiconductor demand. International Journal of Production Research 46:16, pages 4445-4462.
Read now
Read now
H Chen & J E Boylan. (2007) Use of individual and group seasonal indices in subaggregate demand forecasting. Journal of the Operational Research Society 58:12, pages 1660-1671.
Read now
Read now
Articles from other publishers (35)
George Athanasopoulos, Rob J. Hyndman, Nikolaos Kourentzes & Anastasios Panagiotelis. (2024) Forecast reconciliation: A review. International Journal of Forecasting 40:2, pages 430-456.
Crossref
Crossref
Mahdi Abolghasemi, Garth Tarr & Christoph Bergmeir. (2024) Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions. International Journal of Forecasting 40:2, pages 597-615.
Crossref
Crossref
Sajjad Taghiyeh, David C. Lengacher, Amir Hossein Sadeghi, Amirreza Sahebi-Fakhrabad & Robert B. Handfield. (2023) A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management. Supply Chain Analytics 3, pages 100032.
Crossref
Crossref
Mads E. Hansen, Nystrup Peter, Jan K. Møller & Madsen Henrik. (2023) Reconciliation of wind power forecasts in spatial hierarchies. Wind Energy 26:6, pages 615-632.
Crossref
Crossref
Harsh Anand, Roshanak Nateghi & Negin Alemazkoor. (2023) Bottom-up forecasting: Applications and limitations in load forecasting using smart-meter data. Data-Centric Engineering 4.
Crossref
Crossref
Hakeem‐Ur Rehman, Guohua Wan & Raza Rafique. (2022) A hybrid approach with step‐size aggregation to forecasting hierarchical time series. Journal of Forecasting 42:1, pages 176-192.
Crossref
Crossref
Livio Fenga. 2022. Studies in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics
185
218
.
Shouxiang Wang, Xinyu Deng, Haiwen Chen, Qingyuan Shi & Di Xu. (2021) A bottom-up short-term residential load forecasting approach based on appliance characteristic analysis and multi-task learning. Electric Power Systems Research 196, pages 107233.
Crossref
Crossref
Anastasios Panagiotelis, George Athanasopoulos, Puwasala Gamakumara & Rob J. Hyndman. (2021) Forecast reconciliation: A geometric view with new insights on bias correction. International Journal of Forecasting 37:1, pages 343-359.
Crossref
Crossref
Sushil Punia, Surya P. Singh & Jitendra K. Madaan. (2020) A cross-temporal hierarchical framework and deep learning for supply chain forecasting. Computers & Industrial Engineering 149, pages 106796.
Crossref
Crossref
Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Kourentzes & Vassilios Assimakopoulos. (2020) Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. Applied Energy 261, pages 114339.
Crossref
Crossref
Han Li & Qihe Tang. (2019) ANALYZING MORTALITY BOND INDEXES VIA HIERARCHICAL FORECAST RECONCILIATION. ASTIN Bulletin 49:3, pages 823-846.
Crossref
Crossref
Zhuang Zheng, Hainan Chen & Xiaowei Luo. (2019) A Kalman filter-based bottom-up approach for household short-term load forecast. Applied Energy 250, pages 882-894.
Crossref
Crossref
Han Li, Hong Li, Yang Lu & Anastasios Panagiotelis. (2019) A forecast reconciliation approach to cause-of-death mortality modeling. Insurance: Mathematics and Economics 86, pages 122-133.
Crossref
Crossref
Milton Soto-Ferrari, Odette Chams-Anturi, Juan P. Escorcia-Caballero, Namra Hussain & Muhammad Khan. 2019. Computational Logistics. Computational Logistics
413
427
.
Clint L.P. Pennings & Jan van Dalen. (2017) Integrated hierarchical forecasting. European Journal of Operational Research 263:2, pages 412-418.
Crossref
Crossref
Han Lin Shang. (2016) Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods. Population Research and Policy Review 36:1, pages 55-84.
Crossref
Crossref
Zhitao Xu, Wenyan Song, Qin Zhang, X.G. Ming, Lina He & Wenjie Liu. (2017) Product Service Demand Forecasting in Hierarchical Service Structure. Procedia CIRP 64, pages 145-150.
Crossref
Crossref
Zlatana D. Nenova & Jerrold H. May. (2016) Determining an optimal hierarchical forecasting model based on the characteristics of the data set: Technical note. Journal of Operations Management 44:1, pages 62-68.
Crossref
Crossref
Jing Zeng. (2016) Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates. Empirica 43:2, pages 415-444.
Crossref
Crossref
Seongmin Moon, Andrew Simpson & Christian Hicks. (2013) The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand. International Journal of Production Economics 143:2, pages 449-454.
Crossref
Crossref
Seongmin Moon. (2013) Predicting the Performance of Forecasting Strategies for Naval Spare Parts Demand: A Machine Learning Approach. Management Science and Financial Engineering 19:1, pages 1-10.
Crossref
Crossref
Steffen C. Eickemeyer, Tim Borcherding, Sebastian Schäfer & Peter Nyhuis. (2013) Validation of data fusion as a method for forecasting the regeneration workload for complex capital goods. Production Engineering 7:2-3, pages 131-139.
Crossref
Crossref
Seongmin Moon, Christian Hicks & Andrew Simpson. (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 140:2, pages 794-802.
Crossref
Crossref
Seong-Min Moon. (2012) The Impact of Demand Features on the Performance of Hierarchical Forecasting : Case Study for Spare parts in the Navy. Korean Management Science Review 29:1, pages 101-114.
Crossref
Crossref
Rob J. Hyndman, Roman A. Ahmed, George Athanasopoulos & Han Lin Shang. (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics & Data Analysis 55:9, pages 2579-2589.
Crossref
Crossref
Argon Chen & Jakey Blue. (2010) Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands. International Journal of Production Economics 128:2, pages 586-602.
Crossref
Crossref
Huijing Chen & John E. Boylan. 2009. Advances in Business and Management Forecasting. Advances in Business and Management Forecasting
173
188
.
Peter Wanke. (2008) Previsão top-down ou bottom-up? Impacto nos níveis de erro e de estoques de segurança. Gestão & Produção 15:2, pages 231-245.
Crossref
Crossref
Shin‐Lian Lo, Fu‐Kwun Wang & James T. Lin. (2008) Forecasting for the LCD monitor market. Journal of Forecasting 27:4, pages 341-356.
Crossref
Crossref
Handik Widiarta, S. Viswanathan & Rajesh Piplani. (2006) On the effectiveness of top‐down strategy for forecasting autoregressive demands. Naval Research Logistics (NRL) 54:2, pages 176-188.
Crossref
Crossref
Peter Wanke & Eduardo Saliby. (2007) Top-down or bottom-up forecasting?. Pesquisa Operacional 27:3, pages 591-605.
Crossref
Crossref
Giulio Zotteri, Matteo Kalchschmidt & Federico Caniato. (2005) The impact of aggregation level on forecasting performance. International Journal of Production Economics 93-94, pages 479-491.
Crossref
Crossref
Byron J. Dangerfield & John S. Morris. (1992) Top-down or bottom-up: Aggregate versus disaggregate extrapolations. International Journal of Forecasting 8:2, pages 233-241.
Crossref
Crossref
Robert Fildes & Charles Beard. (1992) Forecasting Systems for Production and Inventory Control. International Journal of Operations & Production Management 12:5, pages 4-27.
Crossref
Crossref