  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.
