Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit

Chuen Yik Kang, Chin Poo Lee, Kian Ming Lim

Research output: Journal PublicationArticlepeer-review

21 Citations (Scopus)

Abstract

Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.

Original languageEnglish
Article number149
JournalData
Volume7
Issue number11
DOIs
Publication statusPublished - Nov 2022
Externally publishedYes

Keywords

  • Bitcoin
  • CNN
  • convolutional neural network
  • cryptocurrency price prediction
  • Ethereum
  • gated recurrent unit
  • GRU
  • price prediction
  • Ripple

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Information Systems and Management

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