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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

The M5 competition: Background, organization, and implementation
Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos
International Journal of Forecasting (2021) Vol. 38, Iss. 4, pp. 1325-1336
Open Access | Times Cited: 87

Showing 26-50 of 87 citing articles:

Models for forecasting the traffic flow within the city of Ljubljana
Gašper Petelin, Rok Hribar, Gregor Papa
European Transport Research Review (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 9
Exponential smoothing forecasts: taming the bullwhip effect when demand is seasonal
Maximiliano Udenio, Eleni Vatamidou, Jan C. Fransoo
International Journal of Production Research (2022) Vol. 61, Iss. 6, pp. 1796-1813
Open Access | Times Cited: 15
Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions
Mahdi Abolghasemi, Garth Tarr, Christoph Bergmeir
International Journal of Forecasting (2022) Vol. 40, Iss. 2, pp. 597-615
Closed Access | Times Cited: 14
Exploring the representativeness of the M5 competition data
Evangelos Theodorou, Shengjie Wang, Yanfei Kang, et al.
International Journal of Forecasting (2021) Vol. 38, Iss. 4, pp. 1500-1506
Open Access | Times Cited: 17
Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion
Pieter Cawood, Terence L. van Zyl
Forecasting (2022) Vol. 4, Iss. 3, pp. 732-751
Open Access | Times Cited: 11
iETS: State space model for intermittent demand forecasting
Ivan Svetunkov, John E. Boylan
International Journal of Production Economics (2023) Vol. 265, pp. 109013-109013
Open Access | Times Cited: 6
Efficient probabilistic reconciliation of forecasts for real-valued and count time series
Lorenzo Zambon, Dario Azzimonti, Giorgio Corani
Statistics and Computing (2023) Vol. 34, Iss. 1
Open Access | Times Cited: 6
Sales Forecasting Method for Inventory Replenishment Systems of Vehicle Energy Stations Without Stockouts
Fangfang Li, Lijun Sun, Nan Kong, et al.
IEEE Transactions on Engineering Management (2023) Vol. 71, pp. 6568-6580
Closed Access | Times Cited: 6
Feature-based intermittent demand forecast combinations: accuracy and inventory implications
Li Li, Yanfei Kang, Fotios Petropoulos, et al.
International Journal of Production Research (2022) Vol. 61, Iss. 22, pp. 7557-7572
Closed Access | Times Cited: 10
Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect
Keun Hee Lee, Mali Abdollahian, Sergei Schreider, et al.
Mathematics (2023) Vol. 11, Iss. 11, pp. 2502-2502
Open Access | Times Cited: 5
Improving forecasting model for fuzzy time series using the Self-updating clustering and Bi-directional Long Short Term Memory algorithm
Dinh Phamtoan, Nga VoThiHang, Bich PhamThi
Expert Systems with Applications (2023) Vol. 241, pp. 122767-122767
Closed Access | Times Cited: 5
Predicting/hypothesizing the findings of the M5 competition
Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos
International Journal of Forecasting (2021) Vol. 38, Iss. 4, pp. 1337-1345
Closed Access | Times Cited: 13
Complex Comparison of Statistical and Econometrics Methods for Sales Forecasting
Олександр Косован, Myroslav Datsko
Lecture notes in networks and systems (2024), pp. 340-355
Closed Access | Times Cited: 1
Lumpy and intermittent retail demand forecasts with score-driven models
Rodrigo Sarlo, Cristiano Fernandes, Denis Borenstein
European Journal of Operational Research (2022) Vol. 307, Iss. 3, pp. 1146-1160
Closed Access | Times Cited: 8
The NEON Ecological Forecasting Challenge
R. Quinn Thomas, Carl Boettiger, Cayelan C. Carey, et al.
Authorea (Authorea) (2022)
Open Access | Times Cited: 8
Do we want coherent hierarchical forecasts, or minimal MAPEs or MAEs? (We won’t get both!)
Stephan Kolassa
International Journal of Forecasting (2022) Vol. 39, Iss. 4, pp. 1512-1517
Closed Access | Times Cited: 8
Hierarchical forecasting at scale
Olivier Sprangers, Wander Wadman, Sebastian Schelter, et al.
International Journal of Forecasting (2024) Vol. 40, Iss. 4, pp. 1689-1700
Open Access | Times Cited: 1
Towards Making Learnware Specification and Market Evolvable
J. M. Liu, Zhihao Tan, Zhi‐Hua Zhou
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 12, pp. 13909-13917
Open Access | Times Cited: 1
Cross-temporal forecast reconciliation at digital platforms with machine learning
Jeroen V.K. Rombouts, Marie Ternes, Ines Wilms
International Journal of Forecasting (2024) Vol. 41, Iss. 1, pp. 321-344
Open Access | Times Cited: 1
The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review
Manuel Muth, Michael Lingenfelder, Gerd Nufer
Management Review Quarterly (2024)
Open Access | Times Cited: 1
Investigating the Accuracy of Autoregressive Recurrent Networks Using Hierarchical Aggregation Structure-Based Data Partitioning
José Manuel Oliveira, Patrícia Ramos
(2023)
Open Access | Times Cited: 3
Robust Sales Forecasting Using Deep Learning with Static and Dynamic Covariates
Patrícia Ramos, José Manuel Oliveira
(2023)
Open Access | Times Cited: 3
A Comparison of Statistical and Machine Learning Approaches for Time Series Forecasting in a Demand Management Scenario
Anton Pfeifer, Hendrik Brand, Volker Lohweg
(2023) Vol. 52, pp. 1-6
Closed Access | Times Cited: 3
On the update frequency of univariate forecasting models
Evangelos Spiliotis, Fotios Petropoulos
European Journal of Operational Research (2023) Vol. 314, Iss. 1, pp. 111-121
Closed Access | Times Cited: 3
A solution for M5 Forecasting - Uncertainty: Hybrid gradient boosting and autoregressive recurrent neural network for quantile estimation
Ernest Chiew, Shin Siang Choong
International Journal of Forecasting (2022) Vol. 38, Iss. 4, pp. 1442-1447
Closed Access | Times Cited: 5

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