© 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 original||Inglés estadounidense|
|Estado||Publicada - 1 oct 2019|
|Publicado de forma externa||Sí|
|Evento||BESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings - |
Duración: 1 oct 2019 → …
|Conferencia||BESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings|
|Período||1/10/19 → …|