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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:

Cryptocurrency forecasting with deep learning chaotic neural networks
Salim Lahmiri, Stelios Bekiros
Chaos Solitons & Fractals (2018) Vol. 118, pp. 35-40
Closed Access | Times Cited: 317

Showing 1-25 of 317 citing articles:

Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques
Aytaç Altan, Seçkin Karasu, Stelios Bekiros
Chaos Solitons & Fractals (2019) Vol. 126, pp. 325-336
Closed Access | Times Cited: 364
Quantile connectedness in the cryptocurrency market
Elie Bouri, Tareq Saeed, Xuan Vinh Vo, et al.
Journal of International Financial Markets Institutions and Money (2021) Vol. 71, pp. 101302-101302
Closed Access | Times Cited: 247
Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning
Franco Valencia, Alfonso Gómez-Espinosa, Benjamín Valdés-Aguirre
Entropy (2019) Vol. 21, Iss. 6, pp. 589-589
Open Access | Times Cited: 220
Forecasting and trading cryptocurrencies with machine learning under changing market conditions
Helder Sebasti�ão, Pedro Godinho
Financial Innovation (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 167
Stochastic Neural Networks for Cryptocurrency Price Prediction
Jay Patel, Vasu Kalariya, Pushpendra Parmar, et al.
IEEE Access (2020) Vol. 8, pp. 82804-82818
Open Access | Times Cited: 166
Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators
S. Alonso Monsalve, Andrés L. Suárez‐Cetrulo, Alejandro Cervantes, et al.
Expert Systems with Applications (2020) Vol. 149, pp. 113250-113250
Closed Access | Times Cited: 158
Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey
Ahmed M. Khedr, Ifra Arif, Pravija Raj P V, et al.
Intelligent Systems in Accounting Finance & Management (2021) Vol. 28, Iss. 1, pp. 3-34
Closed Access | Times Cited: 119
Blockchain for deep learning: review and open challenges
Muhammad Shafay, Raja Wasim Ahmad, Khaled Salah, et al.
Cluster Computing (2022) Vol. 26, Iss. 1, pp. 197-221
Open Access | Times Cited: 104
Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction
Azeez A. Oyedele, Anuoluwapo Ajayi, Lukumon O. Oyedele, et al.
Expert Systems with Applications (2022) Vol. 213, pp. 119233-119233
Open Access | Times Cited: 76
On technical trading and social media indicators for cryptocurrency price classification through deep learning
Marco Ortu, Nicola Uras, Claudio Conversano, et al.
Expert Systems with Applications (2022) Vol. 198, pp. 116804-116804
Open Access | Times Cited: 70
Cryptocurrency Market Analysis from the Open Innovation Perspective
Alexey Mikhaylov
Journal of Open Innovation Technology Market and Complexity (2020) Vol. 6, Iss. 4, pp. 197-197
Open Access | Times Cited: 128
Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods
Saeed Nosratabadi, Amirhosein Mosavi, Puhong Duan, et al.
Mathematics (2020) Vol. 8, Iss. 10, pp. 1799-1799
Open Access | Times Cited: 121
An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning
Reaz A. Chowdhury, M. Arifur Rahman, M. Sohel Rahman, et al.
Physica A Statistical Mechanics and its Applications (2020) Vol. 551, pp. 124569-124569
Open Access | Times Cited: 108
Bitcoin Price Prediction and Analysis Using Deep Learning Models
Temesgen Awoke, Minakhi Rout, Lipika Mohanty, et al.
Lecture notes in networks and systems (2020), pp. 631-640
Closed Access | Times Cited: 94
Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels
Zhuorui Zhang, Hong‐Ning Dai, Junhao Zhou, et al.
Expert Systems with Applications (2021) Vol. 183, pp. 115378-115378
Open Access | Times Cited: 84
Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market
Salim Lahmiri, Stelios Bekiros
Chaos Solitons & Fractals (2020) Vol. 133, pp. 109641-109641
Closed Access | Times Cited: 76
Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?
Syed Abul Basher, Perry Sadorsky
Machine Learning with Applications (2022) Vol. 9, pp. 100355-100355
Open Access | Times Cited: 64
How cryptocurrency affects economy? A network analysis using bibliometric methods
Yue Yao, Xuerong Li, Dingxuan Zhang, et al.
International Review of Financial Analysis (2021) Vol. 77, pp. 101869-101869
Closed Access | Times Cited: 63
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 Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data
Gyeongho Kim, Dong-Hyun Shin, Jae Gyeong Choi, et al.
IEEE Access (2022) Vol. 10, pp. 56232-56248
Open Access | Times Cited: 45
MLP-based Learnable Window Size for Bitcoin price prediction
Shahab Rajabi, Pardis Roozkhosh, Nasser Motahari Farimani
Applied Soft Computing (2022) Vol. 129, pp. 109584-109584
Closed Access | Times Cited: 43
Forecasting Bitcoin returns with long short-term memory networks and wavelet decomposition: A comparison of several market determinants
Navid Parvini, Mahsa Abdollahi, Sattar Seifollahi, et al.
Applied Soft Computing (2022) Vol. 121, pp. 108707-108707
Closed Access | Times Cited: 40
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
Frequency connectedness between FinTech, NFT and DeFi: Considering linkages to investor sentiment
Samet Günay, John W. Goodell, Shahnawaz Muhammed, et al.
International Review of Financial Analysis (2023) Vol. 90, pp. 102925-102925
Closed Access | Times Cited: 31
LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios
Andrés García-Medina, Ester Aguayo-Moreno
Computational Economics (2023) Vol. 63, Iss. 4, pp. 1511-1542
Open Access | Times Cited: 29
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