Shengjie Wang, Yanfei Kang & Fotios Petropoulos. (2024) Combining probabilistic forecasts of intermittent demand. European Journal of Operational Research 315:3, pages 1038-1048.
Crossref
Corey Ducharme, Bruno Agard & Martin Trépanier. (2024) Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering. Journal of Forecasting.
Crossref
Leonid Lyubchyk & Galyna Grinberg. (2024) Intermittent Demand Forecasting with Adaptive Bias Correction Based on the Modified Corston Method. SSRN Electronic Journal.
Crossref
Diksha Shrivastava, Sarthak Pujari, Yatin Katyal, Siddhartha Asthana, Chandrudu K & Aakashdeep Singh. (2023) INFRANET: Forecasting intermittent time series using DeepNet with parameterized conditional demand and size distribution. INFRANET: Forecasting intermittent time series using DeepNet with parameterized conditional demand and size distribution.
G. Peter Zhang, Yusen Xia & Maohua Xie. (2023) Intermittent demand forecasting with transformer neural networks. Annals of Operations Research.
Crossref
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, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, M. Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Gael M. Martin, Andrew B. Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Önkal, Alessia Paccagnini, Anastasios Panagiotelis, Ioannis Panapakidis, Jose M. Pavía, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patrícia Ramos, David E. Rapach, J. James Reade, Bahman Rostami-Tabar, Michał Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramón Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova & Florian Ziel. (2022) Forecasting: theory and practice. International Journal of Forecasting 38:3, pages 705-871.
Crossref
Evangelos Spiliotis, Spyros Makridakis, Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos. (2020) Comparison of statistical and machine learning methods for daily SKU demand forecasting. Operational Research 22:3, pages 3037-3061.
Crossref
E. Afrifa-Yamoah & U.A. Mueller. (2022) Modeling digital camera monitoring count data with intermittent zeros for short-term prediction. Heliyon 8:1, pages e08774.
Crossref
Ali Caner Türkmen, Tim Januschowski, Yuyang Wang & Ali Taylan Cemgil. (2021) Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes. PLOS ONE 16:11, pages e0259764.
Crossref
Evangelos Spiliotis, Spyros Makridakis, Anastasios Kaltsounis & Vassilios Assimakopoulos. (2021) Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data. International Journal of Production Economics 240, pages 108237.
Crossref
Karthikeswaren R, Kanishka Kayathwal, Gaurav Dhama & Ankur Arora. (2021) A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods. A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods.
John Boylan & Aris Syntetos. 2021. Intermittent Demand Forecasting. Intermittent Demand Forecasting
347
364
.
John Boylan & Aris Syntetos. 2021. Intermittent Demand Forecasting. Intermittent Demand Forecasting
117
150
.
M Z Babai, A Tsadiras & C Papadopoulos. (2020) On the empirical performance of some new neural network methods for forecasting intermittent demand. IMA Journal of Management Mathematics 31:3, pages 281-305.
Crossref
Sha Zhu, Willem van Jaarsveld & Rommert Dekker. (2020) Spare parts inventory control based on maintenance planning. Reliability Engineering & System Safety 193, pages 106600.
Crossref
Daniel M. Baquero, Galo Mosquera-Recalde & Sonia Valeria Avilés-Sacoto. 2020. Techniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems. Techniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems
157
190
.
Tugce EKİZ YİLMAZ, Güçkan YAPAR & İdil YAVUZ. (2019) COMPARISON OF ATA METHOD AND CROSTON BASED METHODS ON FORECASTING OF INTERMITTENT DEMANDKESİKLİ TALEPLERİN TAHMİNLENMESİNDE ATA METOT VE CROSTON TEMELLİ METOTLARIN KARŞILAŞTIRILMASI. Mugla Journal of Science and Technology 5:2, pages 49-55.
Crossref
Tongdan Jin. 2019. Reliability Engineering and Service. Reliability Engineering and Service
349
389
.
Adolfo Rene Santa Cruz Rodriguez & Camila Corrêa. (2017) Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso. DYNA 84:203, pages 9-16.
Crossref
Sha Zhu, Rommert Dekker, Willem van Jaarsveld, Rex Wang Renjie & Alex J. Koning. (2017) An improved method for forecasting spare parts demand using extreme value theory. European Journal of Operational Research 261:1, pages 169-181.
Crossref
Clint L.P. Pennings, Jan van Dalen & Erwin A. van der Laan. (2017) Exploiting elapsed time for managing intermittent demand for spare parts. European Journal of Operational Research 258:3, pages 958-969.
Crossref
Xiao-Sheng Si, Zheng-Xin Zhang & Chang-Hua HuXiao-Sheng Si, Zheng-Xin Zhang & Chang-Hua Hu. 2017. Data-Driven Remaining Useful Life Prognosis Techniques. Data-Driven Remaining Useful Life Prognosis Techniques
405
417
.
Adriano O. Solis. 2016. Supply Management Research. Supply Management Research
79
95
.
Ming Ding, Xiao-Sheng Si, Qi Zhang & Tianmei Li. (2015) An adaptive spare parts demand forecasting method based on degradation modeling. An adaptive spare parts demand forecasting method based on degradation modeling.
Aris A. Syntetos, M. Zied Babai & Everette S. GardnerJr.Jr.. (2015) Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping. Journal of Business Research 68:8, pages 1746-1752.
Crossref
Nikolaos Kourentzes. (2014) On intermittent demand model optimisation and selection. International Journal of Production Economics 156, pages 180-190.
Crossref
D. Lengu, A.A. Syntetos & M.Z. Babai. (2014) Spare parts management: Linking distributional assumptions to demand classification. European Journal of Operational Research 235:3, pages 624-635.
Crossref
Nikolaos Kourentzes. (2013) Intermittent demand forecasts with neural networks. International Journal of Production Economics 143:1, pages 198-206.
Crossref
Matthew Lindsey & Robert Pavur. 2013. Advances in Business and Management Forecasting. Advances in Business and Management Forecasting
185
195
.
M. Zied Babai, Mohammad M. Ali & Konstantinos Nikolopoulos. (2012) Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis. Omega 40:6, pages 713-721.
Crossref
Nezih Altay, Lewis A. Litteral & Frank Rudisill. (2012) Effects of correlation on intermittent demand forecasting and stock control. International Journal of Production Economics 135:1, pages 275-283.
Crossref
Ulrich Küsters & Jan Speckenbach. 2012. Prognoserechnung. Prognoserechnung
75
108
.
Wenbin Wang & Aris A. Syntetos. (2011) Spare parts demand: Linking forecasting to equipment maintenance. Transportation Research Part E: Logistics and Transportation Review 47:6, pages 1194-1209.
Crossref
Aris A. Syntetos & John E. Boylan. 2011. Service Parts Management. Service Parts Management
1
30
.
Zhang Jiantong & Lv Biyu. (2009) Forecasting Intermittent Demand Based on Grey Theory. Forecasting Intermittent Demand Based on Grey Theory.
John E. Boylan & Aris A. Syntetos. 2008. Complex System Maintenance Handbook. Complex System Maintenance Handbook
479
506
.
Ebenezer Afrifa-Yamoah & Ute A. Mueller. (2021) Modeling Digital Camera Monitoring Count Data with Intermittent Zeros for Short-Term Prediction. SSRN Electronic Journal.
Crossref