A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction
This work addresses the problem of predicting Bitcoin prices for investors, offering an automated alternative to manual feature engineering, though it is incremental in improving existing methods.
The authors tackled Bitcoin price prediction by mining patterns from transaction data using a k-order transaction graph to automatically encode features, and their proposed method outperformed state-of-the-art approaches in experiments.
Bitcoin, as one of the most popular cryptocurrency, is recently attracting much attention of investors. Bitcoin price prediction task is consequently a rising academic topic for providing valuable insights and suggestions. Existing bitcoin prediction works mostly base on trivial feature engineering, that manually designs features or factors from multiple areas, including Bticoin Blockchain information, finance and social media sentiments. The feature engineering not only requires much human effort, but the effectiveness of the intuitively designed features can not be guaranteed. In this paper, we aim to mining the abundant patterns encoded in bitcoin transactions, and propose k-order transaction graph to reveal patterns under different scope. We propose the transaction graph based feature to automatically encode the patterns. A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period. The results of comparison experiments demonstrate that the proposed method outperforms the most recent state-of-art methods.