Cryptocurrencies Price Prediction Using Weighted Memory Multi-channels

Zhuorui ZHANG, Junhao ZHOU, Yanan SONG, Hong Ning DAI*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)

Abstract

After the invention of Bitcoin and a peer to peer electronic cash system based on the blockchain, the market of cryptocurrencies increases rapidly and attracts substantial interest from investors and researchers. Cryptocurrencies price volatility prediction is a challenging task owing to the high stochasticity of the markets. Econometric, machine learning and deep learning models are investigated to tackle the stochastic financial prices fluctuation and to improve the prediction accuracy. Although the introduction of exogenous factors such as macro-financial indicators and blockchain information helps the model prediction more accurately, the noise and effects from markets and political conditions are difficult to interpret and modelling. Inspired by the evidence of strong correlations among cryptocurrencies examined in previous studies, we originally propose a Weighted Memory Channels Regression (WMCR) model to predict the daily close price of cryptocurrencies. The proposed model receives time series of several heavyweight cryptocurrencies price and learns the interdependencies of them by recalibrating the weights of each sequence wisely. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) components are exploited to establish memory and extract spatial and temporal features. Moreover, regularization methods including kernel regularizers and bias regularizers and Dropout method are exploited to improve the generalization ability of the proposed model. A battery of experiments are conducted in this paper. The results present that the WMCR model achieves the state-of-art performance and outperforms other baseline models.

Original languageEnglish
Title of host publicationBlockchain and Trustworthy Systems - Second International Conference, BlockSys 2020, Revised Selected Papers
EditorsZibin Zheng, Hong-Ning Dai, Xiaodong Fu, Benhui Chen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages502-516
Number of pages15
ISBN (Print)9789811592126
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020 - Dali, China
Duration: 6 Aug 20207 Aug 2020

Publication series

NameCommunications in Computer and Information Science
Volume1267
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020
Country/TerritoryChina
CityDali
Period6/08/207/08/20

Bibliographical note

Funding Information:
Acknowledgments. The work described in this paper was partially supported by Macao Science and Technology Development Fund under Macao Funding Scheme for Key R & D Projects (0025/2019/AKP).

Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.

Keywords

  • Blockchain
  • Convolutional neural network
  • Cryptocurencies price prediction
  • Deep learning
  • Long short-term memory
  • Weighted memory channels

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