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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 uncertainty competition: Results, findings and conclusions
Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, et al.
International Journal of Forecasting (2021) Vol. 38, Iss. 4, pp. 1365-1385
Open Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

Forecasting: theory and practice
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, et al.
International Journal of Forecasting (2022) Vol. 38, Iss. 3, pp. 705-871
Open Access | Times Cited: 542
M5 accuracy competition: Results, findings, and conclusions
Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos
International Journal of Forecasting (2022) Vol. 38, Iss. 4, pp. 1346-1364
Open Access | Times Cited: 251
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentín Flunkert, et al.
ACM Computing Surveys (2022) Vol. 55, Iss. 6, pp. 1-36
Open Access | Times Cited: 124
Forecast combinations: An over 50-year review
Xiaoqian Wang, Rob Hyndman, Feng Li, et al.
International Journal of Forecasting (2022) Vol. 39, Iss. 4, pp. 1518-1547
Open Access | Times Cited: 122
A review of predictive uncertainty estimation with machine learning
Hristos Tyralis, Georgia Papacharalampous
Artificial Intelligence Review (2024) Vol. 57, Iss. 4
Open Access | Times Cited: 20
Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector
Felipe Leite Coelho da Silva, Kleyton da Costa, Paulo Canas Rodrigues, et al.
Energies (2022) Vol. 15, Iss. 2, pp. 588-588
Open Access | Times Cited: 43
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
Florian Stadtmann, Adil Rasheed, Trond Kvamsdal, et al.
IEEE Access (2023) Vol. 11, pp. 110762-110795
Open Access | Times Cited: 27
Machine Learning for Master Production Scheduling: Combining probabilistic forecasting with stochastic optimisation
Sebastian Paull, Alexander Bubak, Heiner Stuckenschmidt
Expert Systems with Applications (2025), pp. 126586-126586
Open Access
Demand forecasting in supply chains: a review of aggregation and hierarchical approaches
M. Zied Babai, John E. Boylan, Bahman Rostami-Tabar
International Journal of Production Research (2021) Vol. 60, Iss. 1, pp. 324-348
Open Access | Times Cited: 39
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
Thabang Mathonsi, Terence L. van Zyl
Forecasting (2021) Vol. 4, Iss. 1, pp. 1-25
Open Access | Times Cited: 31
Evaluation of interpretability methods for multivariate time series forecasting
Ozan Ozyegen, Igor Ilic, Mücahit Çevik
Applied Intelligence (2021) Vol. 52, Iss. 5, pp. 4727-4743
Open Access | Times Cited: 27
Using AI to detect panic buying and improve products distribution amid pandemic
Yossiri Adulyasak, Omar Benomar, Ahmed Chaouachi, et al.
AI & Society (2023) Vol. 39, Iss. 4, pp. 2099-2128
Open Access | Times Cited: 10
Combining probabilistic forecasts of intermittent demand
Shengjie Wang, Yanfei Kang, Fotios Petropoulos
European Journal of Operational Research (2024) Vol. 315, Iss. 3, pp. 1038-1048
Open Access | Times Cited: 3
Evaluating different artificial neural network forecasting approaches for optimizing district heating network operation
Lilli Frison, Simon Gölzhäuser, Moritz Bitterling, et al.
Energy (2024) Vol. 307, pp. 132745-132745
Open Access | Times Cited: 3
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
(2021), pp. 1510-1520
Open Access | Times Cited: 25
Forecasting with gradient boosted trees: augmentation, tuning, and cross-validation strategies
A. David Lainder, Russell D. Wolfinger
International Journal of Forecasting (2022) Vol. 38, Iss. 4, pp. 1426-1433
Closed Access | Times Cited: 17
Probabilistic hierarchical forecasting with deep Poisson mixtures
Kin G. Olivares, Oinam Nganba Meetei, Ruijun Ma, et al.
International Journal of Forecasting (2023) Vol. 40, Iss. 2, pp. 470-489
Open Access | Times Cited: 9
Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors
Yashon O. Ouma, Ditiro B. Moalafhi, George Anderson, et al.
Sustainability (2022) Vol. 14, Iss. 22, pp. 14934-14934
Open Access | Times Cited: 15
Supervised learning for integrated forecasting and inventory control
Joost F. van der Haar, Arnoud P. Wellens, Robert Boute, et al.
European Journal of Operational Research (2024) Vol. 319, Iss. 2, pp. 573-586
Closed Access | Times Cited: 2
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
Applicability of the M5 to Forecasting at Walmart
Brian Seaman, John Bowman
International Journal of Forecasting (2021) Vol. 38, Iss. 4, pp. 1468-1472
Closed Access | Times Cited: 14
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
Commentary on the M5 forecasting competition
Stephan Kolassa
International Journal of Forecasting (2021) Vol. 38, Iss. 4, pp. 1562-1568
Open Access | Times Cited: 13
Boosted Embeddings for Time-Series Forecasting
Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, et al.
Lecture notes in computer science (2022), pp. 1-14
Closed Access | Times Cited: 9
Stacking for Probabilistic Short-Term Load Forecasting
Grzegorz Dudek
Lecture notes in computer science (2024), pp. 3-18
Closed Access | Times Cited: 1
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