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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:

Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting
Chiun-Sin Lin, Sheng-Hsiung Chiu, Tzu-Yu Lin
Economic Modelling (2012) Vol. 29, Iss. 6, pp. 2583-2590
Closed Access | Times Cited: 141

Showing 1-25 of 141 citing articles:

Computational Intelligence and Financial Markets: A Survey and Future Directions
Rodolfo C. Cavalcante, Rodrigo C. Brasileiro, Victor L. F. Souza, et al.
Expert Systems with Applications (2016) Vol. 55, pp. 194-211
Closed Access | Times Cited: 527
Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model
Zhenhai Guo, Weigang Zhao, Haiyan Lu, et al.
Renewable Energy (2011) Vol. 37, Iss. 1, pp. 241-249
Closed Access | Times Cited: 501
Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression
Bangzhu Zhu, Dong Han, Ping Wang, et al.
Applied Energy (2017) Vol. 191, pp. 521-530
Open Access | Times Cited: 242
A variational mode decompoisition approach for analysis and forecasting of economic and financial time series
Salim Lahmiri
Expert Systems with Applications (2016) Vol. 55, pp. 268-273
Closed Access | Times Cited: 183
Stock Market Forecasting Using Computational Intelligence: A Survey
Gourav Kumar, Sanjeev Jain, Uday Pratap Singh
Archives of Computational Methods in Engineering (2020) Vol. 28, Iss. 3, pp. 1069-1101
Closed Access | Times Cited: 143
A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example
Jun Hao, Qian Feng, Jianping Li, et al.
Journal of Forecasting (2023) Vol. 42, Iss. 6, pp. 1385-1406
Closed Access | Times Cited: 40
A multiscale analysis for carbon price drivers
Bangzhu Zhu, Shunxin Ye, Dong Han, et al.
Energy Economics (2018) Vol. 78, pp. 202-216
Closed Access | Times Cited: 140
A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China
Shuai Wang, Lean Yu, Ling Tang, et al.
Energy (2011) Vol. 36, Iss. 11, pp. 6542-6554
Closed Access | Times Cited: 131
Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression
Kuo-Ping Lin, Ping‐Feng Pai
Journal of Cleaner Production (2015) Vol. 134, pp. 456-462
Closed Access | Times Cited: 113
Soft computing hybrids for FOREX rate prediction: A comprehensive review
Dadabada Pradeepkumar, Vadlamani Ravi
Computers & Operations Research (2018) Vol. 99, pp. 262-284
Closed Access | Times Cited: 89
An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
Gourav Kumar, Uday Pratap Singh, Sanjeev Jain
Soft Computing (2022) Vol. 26, Iss. 22, pp. 12115-12135
Open Access | Times Cited: 41
Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches
Gang Xie, Shouyang Wang, Kin Keung Lai
Journal of Air Transport Management (2014) Vol. 37, pp. 20-26
Closed Access | Times Cited: 86
Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM 2.5 concentration forecasting
Mingfei Niu, Kai Gan, Shaolong Sun, et al.
Journal of Environmental Management (2017) Vol. 196, pp. 110-118
Closed Access | Times Cited: 80
Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting
Zhiyu Wang, Jun Qiu, Fang‐Fang Li
Water (2018) Vol. 10, Iss. 7, pp. 853-853
Open Access | Times Cited: 80
Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression
Noemi Nava, Tiziana Di Matteo, Tomaso Aste
Risks (2018) Vol. 6, Iss. 1, pp. 7-7
Open Access | Times Cited: 70
Support Vector Regression Model for the prediction of Loadability Margin of a Power System
M. V. Suganyadevi, C. K. Babulal
Applied Soft Computing (2014) Vol. 24, pp. 304-315
Closed Access | Times Cited: 69
Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology
Gang Xie, Ning Zhang, Shouyang Wang
Transportation Research Part E Logistics and Transportation Review (2017) Vol. 108, pp. 160-178
Closed Access | Times Cited: 64
A novel hybrid model to forecast the stock price based on CEEMDAN and support vector regression
Diaa S. Metwally, Muhammad Ali, Safar M. Alghamdi, et al.
Journal of Radiation Research and Applied Sciences (2025) Vol. 18, Iss. 2, pp. 101385-101385
Closed Access
Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques
Vasilios Plakandaras, Théophilos Papadimitriou, Periklis Gogas
Journal of Forecasting (2015) Vol. 34, Iss. 7, pp. 560-573
Closed Access | Times Cited: 58
Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series
Özgür Kişi, Levent Latifoğlu, Fatma Lati̇foğlu
Water Resources Management (2014) Vol. 28, Iss. 12, pp. 4045-4057
Closed Access | Times Cited: 58
A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting
Lean Yu, Wei Dai, Ling Tang, et al.
Neural Computing and Applications (2015) Vol. 27, Iss. 8, pp. 2193-2215
Closed Access | Times Cited: 55
Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price
Kaijian He, Rui Zha, Jun Wu, et al.
Sustainability (2016) Vol. 8, Iss. 4, pp. 387-387
Open Access | Times Cited: 52
An improved local characteristic-scale decomposition to restrict end effects, mode mixing and its application to extract incipient bearing fault signal
Lei Wang, Zhiwen Liu
Mechanical Systems and Signal Processing (2021) Vol. 156, pp. 107657-107657
Closed Access | Times Cited: 33
Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting
Gourav Kumar, Uday Pratap Singh, Sanjeev Jain
International Journal of Intelligent Systems (2021) Vol. 36, Iss. 9, pp. 4902-4935
Closed Access | Times Cited: 32
Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption
Xingpo Liu, Yiqing Zhang, Qichen Zhang
Journal of Hydroinformatics (2022) Vol. 24, Iss. 3, pp. 535-558
Open Access | Times Cited: 27
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