Functional classification of bitcoin wallets

This work proposes a classification model for predicting the main activity of bitcoin wallets based on their balances. Since the balances are a function of time, we apply functional data analysis methods; more specifically, the features of the proposed models are the functional principal components. The estimation of functional principal components is explained in detail. Classifying bitcoin wallets is a relevant problem for two main reasons: to understand how the bitcoin market works, and to identify accounts used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.