# Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction

arXiv: https://arxiv.org/abs/2205.00974 · HTML: https://mlsanity.com/paper/2205.00974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://mlsanity.com/paper/2101.02118/) (2021)
- [ChainNet: Learning on Blockchain Graphs with Topological Features](https://mlsanity.com/paper/1908.06971/) (2019)
- [Bitcoin Price Prediction: An ARIMA Approach](https://mlsanity.com/paper/1904.05315/) (2019)
- [How to Not Get Caught When You Launder Money on Blockchain?](https://mlsanity.com/paper/2010.15082/) (2020)

## 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.