E3S Web Conf.
Volume 218, 20202020 International Symposium on Energy, Environmental Science and Engineering (ISEESE 2020)
|Number of page(s)||5|
|Section||Research on Energy Technology Application and Consumption Structure|
|Published online||11 December 2020|
Bitcoin price prediction using ARIMA and LSTM
Jacobs School of Engineering, University of California San Diego, 92037, US
The goal of this paper is to compare the accuracy of bitcoin price in USD prediction based on two different model, Long Short term Memory (LSTM) network and ARIMA model. Real-time price data is collected by Pycurl from Bitfine. LSTM model is implemented by Keras and TensorFlow. ARIMA model used in this paper is mainly to present a classical comparison of time series forecasting, as expected, it could make efficient prediction limited in short-time interval, and the outcome depends on the time period. The LSTM could reach a better performance, with extra, indispensable time for model training, especially via CPU.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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