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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:

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

Showing 1-25 of 27 citing articles:

Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP
Corne van Zyl, Xianming Ye, Raj Naidoo
Applied Energy (2023) Vol. 353, pp. 122079-122079
Open Access | Times Cited: 44
Explainable multi-step heating load forecasting: Using SHAP values and temporal attention mechanisms for enhanced interpretability
Alexander Neubauer, Stefan Brandt, Martin Kriegel
Energy and AI (2025), pp. 100480-100480
Open Access
Explainable AI: To Reveal the Logic of Black-Box Models
Chinu, Urvashi Bansal
New Generation Computing (2023) Vol. 42, Iss. 1, pp. 53-87
Closed Access | Times Cited: 14
ShapTime: A General XAI Approach for Explainable Time Series Forecasting
Yuyi Zhang, Qiushi Sun, Dongfang Qi, et al.
Lecture notes in networks and systems (2024), pp. 659-673
Closed Access | Times Cited: 4
ITF-GAN: Synthetic time series dataset generation and manipulation by interpretable features
Hendrik Klopries, Andreas Schwung
Knowledge-Based Systems (2023) Vol. 283, pp. 111131-111131
Closed Access | Times Cited: 8
A type-2 neuro-fuzzy system with a novel learning method for Parkinson’s disease diagnosis
Armin Salimi-Badr, Mohammad Hashemi, Hamidreza Saffari
Applied Intelligence (2022) Vol. 53, Iss. 12, pp. 15656-15682
Open Access | Times Cited: 13
An interpretable deep learning multi-dimensional integration framework for exchange rate forecasting based on deep and shallow feature selection and snapshot ensemble technology
Jujie Wang, Ying Dong
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108282-108282
Closed Access | Times Cited: 2
An attention-based deep learning model for multi-horizon time series forecasting by considering periodic characteristic
Jin Fang, Xin Guo, Yujia Liu, et al.
Computers & Industrial Engineering (2023) Vol. 185, pp. 109667-109667
Closed Access | Times Cited: 5
Automatic generation of fuzzy inference systems for multivariate time series forecasting
Thiago Carvalho, Marley Vellasco, José Franco Amaral
Fuzzy Sets and Systems (2023) Vol. 470, pp. 108657-108657
Closed Access | Times Cited: 4
A Spatial–Temporal Gated Network for Credit Card Fraud Detection by Learning Transactional Representations
Yu Xie, Guanjun Liu, MengChu Zhou, et al.
IEEE Transactions on Automation Science and Engineering (2023) Vol. 21, Iss. 4, pp. 6978-6991
Closed Access | Times Cited: 4
Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning
Davood Pirayesh Neghab, Mücahit Çevik, M.I.M. Wahab, et al.
Computational Economics (2024)
Closed Access | Times Cited: 1
On Trustworthy Decision-Making Process of Human Drivers From the View of Perceptual Uncertainty Reduction
Huanjie Wang, Haibin Liu, Wenshuo Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 25, Iss. 2, pp. 1625-1636
Open Access | Times Cited: 3
A comparison of local explanation methods for high-dimensional industrial data: A simulation study
Niklas Fries, Patrik Rydén
Expert Systems with Applications (2022) Vol. 207, pp. 117918-117918
Open Access | Times Cited: 5
Evaluating Explanation Methods of Multivariate Time Series Classification through Causal Lenses
Etienne Vareille, Adel T. Abbas, Michele Linardi, et al.
2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) (2023), pp. 1-10
Open Access | Times Cited: 2
Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models
Md. Khairul Islam
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 21, pp. 23391-23392
Open Access
Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail
Aruna Mogarala Guruvaya, Archana Kollu, B D Parameshachari, et al.
Telecom (2024) Vol. 5, Iss. 3, pp. 537-555
Open Access
Employing Explainable AI Techniques for Air Pollution: An Ante-Hoc and Post-Hoc Approach in Dioxide Nitrogen Forecasting
Pedro Oliveira, FRANCISCO JOSÉ PATRÍCIO FRANCO, Aida Bessa, et al.
Lecture notes in computer science (2024), pp. 325-336
Closed Access
Developing guidelines for functionally-grounded evaluation of explainable artificial intelligence using tabular data
Mythreyi Velmurugan, Chun Ouyang, Yue Xu, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 141, pp. 109772-109772
Open Access
An Interpretability Evaluation Framework for Decision Tree Surrogate Model-Based XAIs
Xinyue Yang, Hai Huang, Xingquan Zuo
(2024), pp. 99-112
Closed Access
Local Interpretability Methods for Time Series Modeling
Ozan Ozyegen
(2024)
Open Access
Local Interpretability Methods for Time Series Modeling
Ozan Ozyegen
(2024)
Open Access
Prediction of future customer needs using machine learning across multiple product categories
David Kilroy, Graham Healy, Simon Caton
PLoS ONE (2024) Vol. 19, Iss. 8, pp. e0307180-e0307180
Open Access
A unified framework for financial commentary prediction
Ozan Ozyegen, Garima Malik, Mücahit Çevik, et al.
Information Technology and Management (2024)
Closed Access
Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods
Jangwon Suh, Jimyeong Kim, Soonil Kwon, et al.
Computers in Biology and Medicine (2024) Vol. 182, pp. 109088-109088
Closed Access
Applying machine learning to electricity price forecasting in simulated energy market scenarios
Felix Nitsch, Christoph Schimeczek, Valentin Bertsch
Energy Reports (2024) Vol. 12, pp. 5268-5279
Closed Access
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