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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 stock time series forecasting approach incorporating candlestick patterns and sequence similarity
Mengxia Liang, Shaocong Wu, Xiaolong Wang, et al.
Expert Systems with Applications (2022) Vol. 205, pp. 117595-117595
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

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: 24
A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimization
Chia‐Hung Wang, Jinchen Yuan, Yingping Zeng, et al.
Applied Intelligence (2024) Vol. 54, Iss. 2, pp. 1770-1797
Closed Access | Times Cited: 11
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: 6
Application of machine learning algorithms in the domain of financial engineering
Xiang Liu, Sultan Salem, Lijun Bian, et al.
Alexandria Engineering Journal (2024) Vol. 95, pp. 94-100
Open Access | Times Cited: 5
Stacked BI-LSTM and E-Optimized CNN-A Hybrid Deep Learning Model for Stock Price Prediction
Swarnalata Rath, Nilima R. Das, Binod Kumar Pattanayak
Optical Memory and Neural Networks (2024) Vol. 33, Iss. 2, pp. 102-120
Closed Access | Times Cited: 5
High-frequency direction forecasting and simulation trading of the crude oil futures using Ichimoku KinkoHyo and Fuzzy Rough Set
Shangkun Deng, Chongyi Xiao, Yingke Zhu, et al.
Expert Systems with Applications (2022) Vol. 215, pp. 119326-119326
Closed Access | Times Cited: 19
Using Historical Pattern Matching and Natural Language Processing in a Hybrid Approach for Stock Market
K. Sri Niharika, C.H. Srisai Naga Satya Mani Pavan, T. Aparna, et al.
(2025), pp. 147-165
Closed Access
Enhancing market trend prediction using convolutional neural networks on Japanese candlestick patterns
Edrees Ramadan Mersal, Kürşat Mustafa Karaoğlan, Hakan Kutucu
PeerJ Computer Science (2025) Vol. 11, pp. e2719-e2719
Open Access
Machine Learning for Bitcoin Price Forecasting Using Kline and Averaged Bars Candlesticks
Ahmed El Youssefi, Abdelaaziz Hessane, Imad Zeroual, et al.
Advances in computational intelligence and robotics book series (2025), pp. 217-232
Closed Access
A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis
Youwei Chen, P. W. Zhao, Zhen Zhang, et al.
International Journal of Computational Intelligence Systems (2022) Vol. 15, Iss. 1
Open Access | Times Cited: 12
Navigating the technical analysis in stock markets: Insights from bibliometric and topic modeling approaches
Sarveshwar Kumar Inani, Harsh Pradhan, Surender Kumar, et al.
Investment Management and Financial Innovations (2024) Vol. 21, Iss. 1, pp. 275-288
Open Access | Times Cited: 2
A local multi-granularity fuzzy rough set method for multi-attribute decision making based on MOSSO-LSTM and its application in stock market
Juncheng Bai, Bingzhen Sun, Jin Ye, et al.
Applied Intelligence (2024) Vol. 54, Iss. 7, pp. 5728-5747
Closed Access | Times Cited: 2
Attention based adaptive spatial–temporal hypergraph convolutional networks for stock price trend prediction
Hongyang Su, Xiaolong Wang, Yang Qin, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121899-121899
Closed Access | Times Cited: 6
Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods
Rui Xu, Wenjie Wu, Yanpeng Cai, et al.
Water (2023) Vol. 15, Iss. 5, pp. 845-845
Open Access | Times Cited: 5
Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast
Heng-Chang Zhang, Qing Wu, Feiyan Li, et al.
Axioms (2022) Vol. 11, Iss. 6, pp. 292-292
Open Access | Times Cited: 8
Market Efficiency and Stock Investment Loss Aversion Guide During COVID-19 Pandemic Events: The Case for Applying Data Mining
Yen-Chang Chen, Ying-Sing Liu
SAGE Open (2023) Vol. 13, Iss. 4
Open Access | Times Cited: 4
A framework for timely and accessible long-term forecasting of shale gas production based on time series pattern matching
Yilun Dong, Youzhi Hao, Detang Lu
International Journal of Forecasting (2024)
Closed Access | Times Cited: 1
Cross-modal scenario generation for stock price forecasting using Wasserstein GAN and GCN
Zixu Wang, Bo Wang, You Li, et al.
Applied Soft Computing (2024), pp. 112342-112342
Closed Access | Times Cited: 1
Experimental analysis of similarity measurements for multivariate time series and its application to the stock market
Zhong-Liang Xiang, Rui Wang, Xiang-Ru Yu, et al.
Applied Intelligence (2023) Vol. 53, Iss. 21, pp. 25450-25466
Closed Access | Times Cited: 3
Stock Trend Prediction Using Candlestick Pattern
Divyanshu Bathla, Ashish Garg, Sarika Sarika
Lecture notes in electrical engineering (2023), pp. 235-246
Closed Access | Times Cited: 2
A Solution to Improve the Detection of the Nominal Value of the Financial Market: A Case Study of the Alphabet Stocks
Zhaohua Li, Xinyue Chang
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 1
Open Access
Empirical Study on Multi-Factor Quantitative Stock Selection Model Based on Support Vector Machine
Chengzhao Zhang
(2024), pp. 1-5
Closed Access
Stacked Optimized Ensemble Machine Learning Model for Predicting Stock Trends through Candlestick Chart Analysis with Feature Engineering Approach
R. Sumathi, S Ashokkumar
International Journal of Electronics and Communication Engineering (2024) Vol. 11, Iss. 6, pp. 74-87
Open Access
Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange
Annisa Nurul Puteri, Suryadi Syamsu, Topan Leoni Putra, et al.
Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer (2023) Vol. 22, Iss. 2, pp. 249-260
Open Access | Times Cited: 1
Stock Price Crash Prediction Based on Multimodal Data Machine Learning Models
Yankai Sheng, Yuanyu Qu, Ding Ma
SSRN Electronic Journal (2023)
Closed Access | Times Cited: 1
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