- Brenda Quesada Prallon
This work proposes a classification model for predicting the main activity of bitcoin wallets based on their balances over time. 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, arbitrary features, but this improvement is much lower than expected.
*Texto enviado pelo aluno.
Membros da banca:
- Yuri Fahham Saporito (orientador) - FGV EMAp
- Rodrigo dos Santos Targino - FGV EMAp
- Arthur Bragança - PUC-Rio