Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction
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Abstract (arXiv)
Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our C2RM can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.
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References · 5
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2021Do We Really Need Deep Learning Models for Time Series Forecasting?cited by 45
- 2019ChainNet: Learning on Blockchain Graphs with Topological Featurescited by 8
- 2019Bitcoin Price Prediction: An ARIMA Approachcited by 7
- 2020How to Not Get Caught When You Launder Money on Blockchain?cited by 2
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