A Blockchain Transaction Graph based Machine Learning Method for Bitcoin\n Price Prediction

Abstract

Bitcoin, as one of the most popular cryptocurrency, is recently attracting\nmuch attention of investors. Bitcoin price prediction task is consequently a\nrising academic topic for providing valuable insights and suggestions. Existing\nbitcoin prediction works mostly base on trivial feature engineering, that\nmanually designs features or factors from multiple areas, including Bticoin\nBlockchain information, finance and social media sentiments. The feature\nengineering not only requires much human effort, but the effectiveness of the\nintuitively designed features can not be guaranteed. In this paper, we aim to\nmining the abundant patterns encoded in bitcoin transactions, and propose\nk-order transaction graph to reveal patterns under different scope. We propose\nthe transaction graph based feature to automatically encode the patterns. A\nnovel prediction method is proposed to accept the features and make price\nprediction, which can take advantage from particular patterns from different\nhistory period. The results of comparison experiments demonstrate that the\nproposed method outperforms the most recent state-of-art methods.\n