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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:

An Advanced CNN-LSTM Model for Cryptocurrency Forecasting
Ioannis E. Livieris, Niki Kiriakidou, Stavros Stavroyiannis, et al.
Electronics (2021) Vol. 10, Iss. 3, pp. 287-287
Open Access | Times Cited: 133

Showing 1-25 of 133 citing articles:

Analysis of Bitcoin Price Prediction Using Machine Learning
Junwei Chen
Journal of risk and financial management (2023) Vol. 16, Iss. 1, pp. 51-51
Open Access | Times Cited: 77
Bitcoin price change and trend prediction through twitter sentiment and data volume
Jacques Vella Critien, Albert Gatt, Joshua Ellul
Financial Innovation (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 72
Past, present, and future of the application of machine learning in cryptocurrency research
Yi‐Shuai Ren, Chaoqun Ma, Xiaolin Kong, et al.
Research in International Business and Finance (2022) Vol. 63, pp. 101799-101799
Open Access | Times Cited: 47
A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification
Ezat Ahmadzadeh, Hyunil Kim, Ongee Jeong, et al.
IEEE Access (2022) Vol. 10, pp. 3228-3237
Open Access | Times Cited: 44
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022
Cheng Zhang, Nilam Nur Amir Sjarif, Roslina Ibrahim
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 40
Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models
Jiyang Cheng, Sunil Tiwari, K. B. Djebbouri, et al.
Technological Forecasting and Social Change (2023) Vol. 198, pp. 122938-122938
Closed Access | Times Cited: 38
On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles
Kate Murray, Andrea Rossi, Diego Carraro, et al.
Forecasting (2023) Vol. 5, Iss. 1, pp. 196-209
Open Access | Times Cited: 36
Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural-network approach
Dawei Shang, Zhiqi Yan, Lei Zhang, et al.
Expert Systems with Applications (2023) Vol. 228, pp. 120329-120329
Closed Access | Times Cited: 29
Models used to characterise blockchain features. A systematic literature review and bibliometric analysis
Juan Jesús Rico-Peña, Raquel Arguedas Sanz, Carmen López-Martín
Technovation (2023) Vol. 123, pp. 102711-102711
Open Access | Times Cited: 25
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
Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks
Bhaskar Tripathi, Rakesh Kumar Sharma
Computational Economics (2022) Vol. 62, Iss. 4, pp. 1919-1945
Open Access | Times Cited: 36
Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics
Ryosaku Ota, Fumiyoshi Yamashita
Journal of Controlled Release (2022) Vol. 352, pp. 961-969
Closed Access | Times Cited: 35
Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit
Chuen Yik Kang, Chin Poo Lee, Kian Ming Lim
Data (2022) Vol. 7, Iss. 11, pp. 149-149
Open Access | Times Cited: 32
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
Enhanced Bitcoin Price Direction Forecasting With DQN
Azamjon Muminov, Otabek Sattarov, Daeyoung Na
IEEE Access (2024) Vol. 12, pp. 29093-29112
Open Access | Times Cited: 6
A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets
Emmanuel Pintelas, Ioannis E. Livieris, Panagiotis Pintelas
Sensors (2021) Vol. 21, Iss. 22, pp. 7731-7731
Open Access | Times Cited: 36
Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature
Sina E. Charandabi, Kamyar Kamyar
European Journal of Business Management and Research (2021) Vol. 6, Iss. 6, pp. 17-20
Open Access | Times Cited: 35
A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price
Pavan Kumar Nagula, Christos Alexakis
Journal of Behavioral and Experimental Finance (2022) Vol. 36, pp. 100741-100741
Closed Access | Times Cited: 20
A novel forecasting strategy for improving the performance of deep learning models
Ioannis E. Livieris
Expert Systems with Applications (2023) Vol. 230, pp. 120632-120632
Closed Access | Times Cited: 11
Harnessing technical indicators with deep learning based price forecasting for cryptocurrency trading
Mingu Kang, Jinglan Hong, Suntae Kim
Physica A Statistical Mechanics and its Applications (2025), pp. 130359-130359
Closed Access
Predicting Bull and Bear Markets: A Deep Learning and Linear Regression Study in Cryptocurrencies
João Henrique Inacio de Souza, Rodolfo I. Meneguette, Vinícius P. Gonçalves, et al.
Lecture notes in computer science (2025), pp. 281-295
Closed Access
Helformer: an attention-based deep learning model for cryptocurrency price forecasting
T.O. Kehinde, Oluyinka J. Adedokun, Agnel Praveen Joseph, et al.
Journal Of Big Data (2025) Vol. 12, Iss. 1
Open Access
Risk Forecasting Using Artificial Intelligence and Machine Learning
Shradha Attri, Sanjeev Gupta, Sachin Singh
Advances in computational intelligence and robotics book series (2025), pp. 187-196
Closed Access
A novel decision ensemble framework: Attention-customized BiLSTM and XGBoost for speculative stock price forecasting
Riaz Ud Din, Salman Ahmed, Shahul Khan, et al.
PLoS ONE (2025) Vol. 20, Iss. 4, pp. e0320089-e0320089
Open Access
A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index
Ioannis E. Livieris, Stavros Stavroyiannis, Emmanuel Pintelas, et al.
Evolving Systems (2021) Vol. 13, Iss. 1, pp. 85-100
Closed Access | Times Cited: 27
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