TY - GEN
T1 - Dynamic Analysis of Bitcoin Fluctuations by Means of a Fractal Predictor
AU - Escobar, Jesús Jaime Moreno
AU - Matamoros, Oswaldo Morales
AU - Páez, Ana Lilia Coria
AU - Padilla, Ricardo Tejeida
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Using of cryptocurrency has boomed in recent years, such as Bitcoin, Ethereum or Ripple. It is interesting to have a Bitcoin forecasting tool to try to understand the trends at global economic level. A virtual currency that can be used as a means of payment just like physical money. Any cryptocurrency uses peer-to-peer technology and is not controlled by any economic or political entity, such as a bank or government. In 2009, Bitcoin was conceived and was priced at 0.39 USD reaching its all-time high in 2017 with a price of 17,549.67 USD, i.e. 45 thousand times more in less than 10 years. This work focuses on predicting bitcoin-price trending will have in 2020 by using a Self-Affine Fractal Analysis as a tool of artificial intelligence. The results provided by present work in first 6 months agree with 98% with those actually obtained despite training only with data from first days of time series.
AB - Using of cryptocurrency has boomed in recent years, such as Bitcoin, Ethereum or Ripple. It is interesting to have a Bitcoin forecasting tool to try to understand the trends at global economic level. A virtual currency that can be used as a means of payment just like physical money. Any cryptocurrency uses peer-to-peer technology and is not controlled by any economic or political entity, such as a bank or government. In 2009, Bitcoin was conceived and was priced at 0.39 USD reaching its all-time high in 2017 with a price of 17,549.67 USD, i.e. 45 thousand times more in less than 10 years. This work focuses on predicting bitcoin-price trending will have in 2020 by using a Self-Affine Fractal Analysis as a tool of artificial intelligence. The results provided by present work in first 6 months agree with 98% with those actually obtained despite training only with data from first days of time series.
KW - Bitcoin
KW - Cryptocurrencies
KW - Fluctuations
KW - Fractal predictor
KW - Self-affine analysis
UR - http://www.scopus.com/inward/record.url?scp=85113265555&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-82193-7_53
DO - 10.1007/978-3-030-82193-7_53
M3 - Contribución a la conferencia
AN - SCOPUS:85113265555
SN - 9783030821920
T3 - Lecture Notes in Networks and Systems
SP - 791
EP - 804
BT - Intelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference IntelliSys
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2021
Y2 - 2 September 2021 through 3 September 2021
ER -