  Bitcoin, one of the major cryptocurrencies, presents great opportunities and
challenges with its tremendous potential returns accompanying high risks. The
high volatility of Bitcoin and the complex factors affecting them make the
study of effective price forecasting methods of great practical importance to
financial investors and researchers worldwide. In this paper, we propose a
novel approach called MRC-LSTM, which combines a Multi-scale Residual
Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to
implement Bitcoin closing price prediction. Specifically, the Multi-scale
residual module is based on one-dimensional convolution, which is not only
capable of adaptive detecting features of different time scales in multivariate
time series, but also enables the fusion of these features. LSTM has the
ability to learn long-term dependencies in series, which is widely used in
financial time series forecasting. By mixing these two methods, the model is
able to obtain highly expressive features and efficiently learn trends and
interactions of multivariate time series. In the study, the impact of external
factors such as macroeconomic variables and investor attention on the Bitcoin
price is considered in addition to the trading information of the Bitcoin
market. We performed experiments to predict the daily closing price of Bitcoin
(USD), and the experimental results show that MRC-LSTM significantly
outperforms a variety of other network structures. Furthermore, we conduct
additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to
further confirm the effectiveness of the MRC-LSTM in short-term forecasting for
multivariate time series of cryptocurrencies.
