Bitcoin Price Forecasting via Ensemble-based LSTM Deep Learning Networks
MyungJae Shin, Aziz Mohaisen, Joongheon Kim
Abstract
Time series prediction plays a significant role in the Bitcoin market because of volatile characteristics. Recently, deep neural networks with advanced techniques such as ensembles have led to studies that show successful performance in various fields. In this paper, an ensemble-enabled Long Short-Term Memory (LSTM) with various time interval models is proposed for predicting Bitcoin price. Although hour and minute data set are shown to provide moderate shifts, daily data has relatively a deterministic shift. As such, the ensemble-enabled LSTM network architecture learned the individual characteristics and impact on price predictions from each data set. Experimental results with real-world measurement data show that this learning architecture effectively forecasts prices, especially in risky time such as sudden price fall.