Time-Series Forecasting of Cryptocurrency Prices Using High-Dimensional Features and a Hybrid Approach

Abstract

Nowadays, digital cryptocurrencies are the most popular asset, especially for international exchanges. Bitcoin is the earliest cryptocurrency that succeeded in being used in financial transactions. Bitcoin stores the transactions in Blockchain technology. Bitcoin price has been unstable during the time from 0.5$ to about 60,000$ since 2010. Many efforts exist to predict Bitcoin value or its fluctuations using machine learning techniques. The price prediction is usually more challenging than fluctuations prediction, and its performance metrics are improved. This study introduces a methodology to predict Bitcoin price in a dataset, including four intervals to evaluate the proposed method in different situations. The experimental results show that the generalized linear model and Long Short-Term Memory (LSTM) were the best machine learning techniques. The proposed model outperforms the deep learning baseline model with about 18% and 20% relative improvement in mean absolute error and means absolute percentage error, respectively. Deep learning approaches have achieved much better results than other approaches due to the automatic selection of features. Compared to the results reported in the literature, the 1D-CNN+IndRNN proposed approach has reached 81% accuracy, with an 18% improvement. In the proposed approach, 1D-CNN is responsible for feature extraction and IndRNN is responsible for learning features in the form of time series.