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Using Networks and Partial Differential Equations to Predict Bitcoin Price

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  • Yufang Wang
  • Haiyan Wang

Abstract

Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting bitcoin price. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends Index is incorporated to the PDE model to reflect the effect of bitcoin market sentiment. The experiment shows that the average accuracy of daily bitcoin price prediction is 0.82 for 362 consecutive days in 2017. The results demonstrate the PDE model is capable of predicting bitcoin price. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.

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  • Yufang Wang & Haiyan Wang, 2020. "Using Networks and Partial Differential Equations to Predict Bitcoin Price," Papers 2001.03099, arXiv.org.
    • Handle: RePEc:arx:papers:2001.03099
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    References listed on IDEAS

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    1. repec:men:wpaper:58_2015 is not listed on IDEAS
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    5. Jaroslav Bukovina & Matus Marticek, 2016. "Sentiment and Bitcoin Volatility," MENDELU Working Papers in Business and Economics 2016-58, Mendel University in Brno, Faculty of Business and Economics.
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    7. Cuneyt Akcora & Matthew Dixon & Yulia Gel & Murat Kantarcioglu, 2018. "Bitcoin Risk Modeling with Blockchain Graphs," Papers 1805.04698, arXiv.org.
    8. Koutmos, Dimitrios, 2018. "Bitcoin returns and transaction activity," Economics Letters, Elsevier, vol. 167(C), pages 81-85.
    9. Kurbucz, Marcell Tamás, 2019. "Predicting the price of Bitcoin by the most frequent edges of its transaction network," Economics Letters, Elsevier, vol. 184(C).
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    Cited by:

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    2. Yishun Liu & Chunhua Yang & Keke Huang & Weiping Liu, 2023. "A Multi-Factor Selection and Fusion Method through the CNN-LSTM Network for Dynamic Price Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, February.

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