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Deep Learning-Based Transaction Prediction in Ethereum

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Blockchain and Trustworthy Systems (BlockSys 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1490))

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Abstract

In recent years, deep learning has been applied to data analysis in the financial field. One of the important applications is time series prediction. Meanwhile, with the advent of blockchain technology, cryptocurrencies have attracted attention in the financial field and the public. Therefore, there has been a lot of researches done on the time series prediction of cryptocurrencies. However, most of these studies are about predicting the prices of various cryptocurrencies, lacking in predicting the transaction amount. As platforms for trading cryptocurrencies, cryptocurrency exchanges play an important role in the crypto market. In this paper, we collect the transaction data of 15 exchange addresses on Ethereum which is an open-source public blockchain platform with smart contract functions. By modeling the prediction of the transaction value as a time series prediction problem, we conduct experiments using deep learning-based methods to make predictions. Experimental results show that deep learning is more effective in predicting transaction value compared with traditional methods.

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Notes

  1. 1.

    https://etherscan.io/.

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Acknowledgments

The work described in this paper is supported by the National Key R&D Program of China (2020YFB1006005), the National Natural Science Foundation of China (61973325, U1811462).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, China

    Zhuoming Gu, Dan Lin, Jiatao Zheng & Jiajing Wu

  2. National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou, 510006, China

    Zhuoming Gu, Dan Lin, Jiatao Zheng & Jiajing Wu

  3. WeBank, Shenzhen, 518055, China

    Chaoxin Hu

Authors
  1. Zhuoming Gu
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  2. Dan Lin
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  3. Jiatao Zheng
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  4. Jiajing Wu
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Corresponding author

Correspondence to Jiajing Wu .

Editor information

Editors and Affiliations

  1. Macau University of Science and Technology, Macao, China

    Hong-Ning Dai

  2. Peking University, Beijing, China

    Xuanzhe Liu

  3. Hong Kong Polytechnic University, Hong Kong, China

    Daniel Xiapu Luo

  4. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Jiang Xiao

  5. Sun Yat-sen University, Guangzhou, China

    Xiangping Chen

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Gu, Z., Lin, D., Zheng, J., Wu, J., Hu, C. (2021). Deep Learning-Based Transaction Prediction in Ethereum. In: Dai, HN., Liu, X., Luo, D.X., Xiao, J., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2021. Communications in Computer and Information Science, vol 1490. Springer, Singapore. https://doi.org/10.1007/978-981-16-7993-3_3

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  • DOI: https://doi.org/10.1007/978-981-16-7993-3_3

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  • Online ISBN: 978-981-16-7993-3

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