Comparative study of Bitcoin price prediction using WaveNets, Recurrent Neural Networks and other Machine Learning Methods

Leonardo Felizardo, Roberth Oliveira, Emilio Del-Moral-Hernandez, Fabio Cozman

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

1 Cita (Scopus)

Resumen

© 2019 IEEE. Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with their many variations that can effectively forecast. However, with the recent advancement in the computational capacity of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms have been developed to forecast time series data. This article compares different methodologies such as ARIMA, Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM) and WaveNets for estimating the future price of Bitcoin.
Idioma originalInglés estadounidense
DOI
EstadoPublicada - 1 oct 2019
Publicado de forma externa
EventoBESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings -
Duración: 1 oct 2019 → …

Conferencia

ConferenciaBESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings
Período1/10/19 → …

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    Felizardo, L., Oliveira, R., Del-Moral-Hernandez, E., & Cozman, F. (2019). Comparative study of Bitcoin price prediction using WaveNets, Recurrent Neural Networks and other Machine Learning Methods. Papel presentado en BESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings, . https://doi.org/10.1109/BESC48373.2019.8963009