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Price Prediction of Ethereum Using Time Series and Deep Learning Techniques

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Proceedings of Emerging Trends and Technologies on Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1414))

Abstract

Ethereum, a blockchain platform inspired by Bitcoin, was introduced in 2015. It is a worldwide computing platform fueled by Ether (ETH), its native currency. As the demand for processing power on the Ethereum blockchain rises, so will the price of ETH. Several studies are working to project its price based on previous price inflations of the cryptocurrency. This topic has become a prominent research topic all around the world. In this work, the price of ETH is predicted using a hybrid model consisting of Long short-term memory (LSTM) and Vector Auto Regression (VAR). The hybrid model gave the least values for the evaluation metrics compared to the standalone models.

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Authors and Affiliations

  1. Christ (Deemed to Be University), Lavasa, Pune, India

    Preeti Sharma & R. M. Pramila

Authors
  1. Preeti Sharma
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  2. R. M. Pramila
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Corresponding author

Correspondence to Preeti Sharma .

Editor information

Editors and Affiliations

  1. Education and Training Division, CDAC, Noida, Uttar Pradesh, India

    Arti Noor

  2. Education and Training Division, CDAC, Noida, Uttar Pradesh, India

    Kriti Saroha

  3. Department of Automatic Control, Computers and Electronics, Petroleum-Gas University of Ploiesti, Ploiesti, Romania

    Emil Pricop

  4. Computer Science, and Information Technology, Kwantlen Polytechnic University, Surrey, BC, Canada

    Abhijit Sen

  5. Department of Electronics and Electrical Engineering, IIT Guwahati, Guwahati, Assam, India

    Gaurav Trivedi

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Sharma, P., Pramila, R.M. (2023). Price Prediction of Ethereum Using Time Series and Deep Learning Techniques. In: Noor, A., Saroha, K., Pricop, E., Sen, A., Trivedi, G. (eds) Proceedings of Emerging Trends and Technologies on Intelligent Systems. Advances in Intelligent Systems and Computing, vol 1414. Springer, Singapore. https://doi.org/10.1007/978-981-19-4182-5_32

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