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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:

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period
Abdullah Bin Omar, Shuai Huang, Anas A. Salameh, et al.
Frontiers in Environmental Science (2022) Vol. 10
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

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: 36
An Analysis of the Application and Impact Mechanisms of Machine Learning in the Field of Asset Pricing
M. F. Li
Advances in Economics Management and Political Sciences (2025) Vol. 150, Iss. 1, pp. 13-21
Closed Access
Stock price prediction with attentive temporal convolution-based generative adversarial network
Ying Liu, Xiaohua Huang, Liwei Xiong, et al.
Array (2025) Vol. 25, pp. 100374-100374
Open Access
Utilizing Various Machine Learning Techniques for Diabetes Mellitus Feature Selection and Classification
Alaa Sheta, Walaa H. Elashmawi, Ahmad Al–Qerem, et al.
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 3
Open Access | Times Cited: 2
Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models
Weisi Chen, Walayat Hussain, Francesco Cauteruccio, et al.
Computer Modeling in Engineering & Sciences (2023) Vol. 139, Iss. 1, pp. 187-224
Open Access | Times Cited: 6
Using singular spectrum analysis and empirical mode decomposition to enhance the accuracy of a machine learning-based soil moisture forecasting algorithm
Eduart Murcia, Sandra M. Guzmán
Computers and Electronics in Agriculture (2024) Vol. 224, pp. 109200-109200
Closed Access | Times Cited: 1
A Systematic Review on Graph Neural Network-based Methods for Stock Market Forecasting
Manali Patel, Krupa Jariwala, Chiranjoy Chattopadhyay
ACM Computing Surveys (2024)
Closed Access | Times Cited: 1
Research on the Price Prediction of Bitcoin and Gold Based on Random Forest Model
Jingben Lu, Yawei Song, Qianhui Li, et al.
(2023)
Open Access | Times Cited: 2
Predictive Analysis of S&P BSE Greenex Index: Unlocking Insights for Sustainable Investments
Noella Nazareth, Y.V. Reddy
Australasian Accounting Business and Finance Journal (2024) Vol. 18, Iss. 3, pp. 223-247
Open Access
Forecasting Foreign Direct Investment Inflow to Bangladesh: Using an Autoregressive Integrated Moving Average and a Machine Learning-Based Random Forest Approach
Md. Monirul Islam, Arifa Jannat, Kentaka Aruga, et al.
Journal of risk and financial management (2024) Vol. 17, Iss. 10, pp. 451-451
Open Access
Price prediction of dual-listed stocks with RF and LSTM algorithms: NYSE and BIST comparison
Emine Nihan Cici Karaboğa, Gamze ŞEKEROĞLU, Esra Kızıloğlu, et al.
Mathematical Modelling and Numerical Simulation with Applications (2024) Vol. 4, Iss. 5-Special Issue: ICAME'24, pp. 207-230
Closed Access
The Fundamentals of Stock Market Investing in the Twenty-First Century
Steven Tairu Bello
IntechOpen eBooks (2024)
Open Access
Visualization and forecasting of stock’s closing price using machine learning
Aditi Gupta, Akansha Akansha, Khushboo Joshi, et al.
Multimedia Tools and Applications (2024) Vol. 83, Iss. 29, pp. 72471-72489
Closed Access
A Random Forest Stock Prediction Model Based on Bayesian Optimization
Yajuan Zhang, Xiuyan Zheng, Sihan Yang, et al.
(2024), pp. 42-46
Closed Access
Forecasting the Stock Market Returns Using nonlinear hybrid GARCH-SETAR model
Tayyab Raza Fraz
JISR management and social sciences & economics (2024) Vol. 22, Iss. 1, pp. 31-50
Open Access
Analyzing and forecasting air pollution concentration in the capital and Southern Thailand using a lag-dependent Gaussian process model
Haris Khurram, Apiradee Lim
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 11
Closed Access
Forecasting the Metal Ores Industry Index on the Tehran Stock Exchange: A Gated Recurrent Unit (GRU) Approach
Reza Javadpour Moghadam
Journal of Artificial Intelligence and Capsule Networks (2024) Vol. 6, Iss. 4, pp. 436-451
Closed Access
Picking Winners: Identifying Features of High-Performing Special Purpose Acquisition Companies (SPACs) with Machine Learning
Caleb J. Williams
Journal of risk and financial management (2023) Vol. 16, Iss. 4, pp. 236-236
Open Access | Times Cited: 1
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
arXiv (Cornell University) (2023)
Open Access | Times Cited: 1
Stock Market Prediction using Machine Learning Techniques: A Systematic Review
Aditi Gupta, Akansha Akansha, Khushboo Joshi, et al.
2020 International Conference on Power, Instrumentation, Control and Computing (PICC) (2023), pp. 1-6
Closed Access | Times Cited: 1
Using Singular Spectrum Analysis and Empirical Mode Decomposition to Enhance the Accuracy of a Machine Learning-Based Soil Moisture Forecasting Algorithm
Eduart Murcia, Sandra M. Guzmán
(2023)
Closed Access | Times Cited: 1
Building a Stock Price Prediction Model using Random Forest Regression and Sentimental Analysis
A Yashmita, Kavitha D
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2023) Vol. 07, Iss. 03
Open Access
Forecasting stock indices with the COVID-19 infection rate as an exogenous variable
Mohammad Saha A. Patwary, Kumer Pial Das
PeerJ Computer Science (2023) Vol. 9, pp. e1532-e1532
Open Access
Peer Review #1 of "Forecasting stock indices with the COVID-19 infection rate as an exogenous variable (v0.1)"
Mohammad Patwary, Kumer Pial Das, Mohammad Shaha, et al.
(2023)
Open Access
Peer Review #3 of "Forecasting stock indices with the COVID-19 infection rate as an exogenous variable (v0.2)"
Sumita Halder
(2023)
Open Access
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