|
Activity Number:
|
147
- High-Dimensional Time Series Analysis and Its Applications
|
|
Type:
|
Invited
|
|
Date/Time:
|
Monday, July 30, 2018 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistical Learning and Data Science
|
|
Abstract #326908
|
Presentation
|
|
Title:
|
Understanding Cryptocurrency Price Formation from Time Series of Local Blockchain Graph Features
|
|
Author(s):
|
Cuneyt Akcora* and Asim Dey and Ceren Abay and Yulia Gel and Umar Islambekov and Murat Kantarcioglu
|
|
Companies:
|
University of Texas at Dallas and University of Texas at Dallas and University of Texas at Dallas and University of Texas at Dallas and University of Texas at Dallas and University of Texas at Dallas
|
|
Keywords:
|
blockchain;
chainlets;
complex networks;
topological data analysis;
bitcoin;
high dimensional time series
|
|
Abstract:
|
Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed a flood of attention. With the emergence and rapid adoption of Blockchain and the associated cryptocurrencies understanding the network dynamics behind Blockchain technologies has emerged as an important research direction. Unlike other financial networks such as stock and currency trading, blockchains have the entire time series of interaction graph accessible to the public. This facilitates a thorough analysis of the network in time. A natural question to ask is whether the network dynamics of a cryptocurrency impact its price in dollars. We show that on the one hand, time series of standard global graph features such as degree distribution are not enough to capture the network dynamics that impact the underlying cryptocurrency price. In contrast, multiple time series of persistent homologies can explain higher level interactions among nodes in Blockchain graphs and can be used to build more accurate price prediction models.
|
Authors who are presenting talks have a * after their name.
