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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:

A graph-based CNN-LSTM stock price prediction algorithm with leading indicators
Jimmy Ming‐Tai Wu, Zhongcui Li, Norbert Herencsár, et al.
Multimedia Systems (2021) Vol. 29, Iss. 3, pp. 1751-1770
Open Access | Times Cited: 189

Showing 1-25 of 189 citing articles:

The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction
Parshv Chhajer, Manan Shah, Ameya Kshirsagar
Decision Analytics Journal (2021) Vol. 2, pp. 100015-100015
Open Access | Times Cited: 161
A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China
Hanxiao Shi, Anlei Wei, Xiaozhen Xu, et al.
Journal of Environmental Management (2024) Vol. 352, pp. 120131-120131
Closed Access | Times Cited: 52
Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction
Kinjal Chaudhari, Ankit Thakkar
Expert Systems with Applications (2023) Vol. 219, pp. 119527-119527
Closed Access | Times Cited: 43
Stock Market Analysis and Prediction for Nifty50 using LSTM Deep Learning Approach
Pushpendra Singh Sisodia, Anish Gupta, Yogesh Kumar, et al.
2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (2022), pp. 156-161
Closed Access | Times Cited: 43
AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators
Saleh Albahli, Tahira Nazir, Awais Mehmood, et al.
Electronics (2022) Vol. 11, Iss. 4, pp. 611-611
Open Access | Times Cited: 38
Intelligent forecasting model of stock price using neighborhood rough set and multivariate empirical mode decomposition
Juncheng Bai, Jianfeng Guo, Bingzhen Sun, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106106-106106
Closed Access | Times Cited: 27
GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction
Chenyang Qi, Jiaying Ren, Jin Su
Applied Sciences (2023) Vol. 13, Iss. 12, pp. 7104-7104
Open Access | Times Cited: 24
Data-driven stock forecasting models based on neural networks: A review
Wuzhida Bao, Yuting Cao, Yin Yang, et al.
Information Fusion (2024) Vol. 113, pp. 102616-102616
Open Access | Times Cited: 12
A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach
Pham Hoang Vuong, Lam Hung Phu, Tran Hong Van Nguyen, et al.
Science Progress (2024) Vol. 107, Iss. 1
Open Access | Times Cited: 10
News-driven stock market index prediction based on trellis network and sentiment attention mechanism
Wenjie Liu, Y. F. Ge, Yuchen Gu
Expert Systems with Applications (2024) Vol. 250, pp. 123966-123966
Closed Access | Times Cited: 8
Enhancing Drought Forecast Accuracy Through Informer Model Optimization
Jieru Wei, Wenwu Tang, Pakorn Ditthakit, et al.
Land (2025) Vol. 14, Iss. 1, pp. 126-126
Open Access | Times Cited: 1
Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric
Jean Dessain
Expert Systems with Applications (2022) Vol. 199, pp. 116970-116970
Open Access | Times Cited: 37
Deep learning, graph-based text representation and classification: a survey, perspectives and challenges
Phu Pham, Loan T. T. Nguyen, Witold Pedrycz, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 6, pp. 4893-4927
Closed Access | Times Cited: 37
Stocks of year 2020: prediction of high variations in stock prices using LSTM
Gourav Bathla, Rinkle Rani, Himanshu Aggarwal
Multimedia Tools and Applications (2022) Vol. 82, Iss. 7, pp. 9727-9743
Closed Access | Times Cited: 32
A multi-agent reinforcement learning framework for optimizing financial trading strategies based on TimesNet
Yuling Huang, Chujin Zhou, Kai Cui, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121502-121502
Closed Access | Times Cited: 21
A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis
Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, et al.
IEEE Access (2023) Vol. 11, pp. 125359-125380
Open Access | Times Cited: 20
Attention-based CNN–LSTM for high-frequency multiple cryptocurrency trend prediction
Peng Peng, Yuehong Chen, Weiwei Lin, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121520-121520
Closed Access | Times Cited: 19
Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market
Hassan Oukhouya, Khalid El Himdi
(2023), pp. 39-39
Open Access | Times Cited: 18
A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading
Yuling Huang, Xiaoxiao Wan, Lin Zhang, et al.
Expert Systems with Applications (2023) Vol. 240, pp. 122581-122581
Closed Access | Times Cited: 18
A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networks
Jie Zou, Jiashu Lou, Baohua Wang, et al.
Expert Systems with Applications (2023) Vol. 242, pp. 122801-122801
Open Access | Times Cited: 16
A Stock Index Futures Price Prediction Approach Based on the MULTI-GARCH-LSTM Mixed Model
Haojun Pan, Yuxiang Tang, Guoqiang Wang
Mathematics (2024) Vol. 12, Iss. 11, pp. 1677-1677
Open Access | Times Cited: 6
Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review
Ivan Malashin, В С Тынченко, Andrei Gantimurov, et al.
Polymers (2024) Vol. 16, Iss. 18, pp. 2607-2607
Open Access | Times Cited: 6
GCNET: Graph-based prediction of stock price movement using graph convolutional network
Alireza Jafari, Saman Haratizadeh
Engineering Applications of Artificial Intelligence (2022) Vol. 116, pp. 105452-105452
Open Access | Times Cited: 23
Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price
Wenyang Huang, Jianyu Zhao, Xiaokang Wang
Energy Economics (2024) Vol. 132, pp. 107459-107459
Closed Access | Times Cited: 5
Forecasting of Stock Market from Borsa Istanbul Banks Using MGRU with Modified Jellyfish Search Optimization Algorithms
R. Mahaveerakannan, Cuddapah Anitha, S. Devi
Lecture notes in networks and systems (2025), pp. 275-288
Closed Access
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