Comparing different deep learning models for cryptocurrency forecasting

In recent years, cryptocurrencies have become more and more popular and recognized, and they have the characteristics of financial products and are involved in many financial transactions. Therefore, cryptocurrency trading is generally considered one of the most popular and promising types of profitability. However, this growing financial market is unstable, so this paper aims to develop a relatively accurate and reliable prediction model. This paper compares and analyzes various neural networks and traditional analytical methods based on the need to forecast cryptocurrency prices. Finally, this paper uses a variety of deep learning neural networks, including Recurrent neural network (RNN), Long Short-Term Memory (LSTM) network, Gate Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The data of three cryptocurrencies with the highest market capitalization, largest size, and best known were selected, namely Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). We collect the three-cryptocurrency data for five consecutive years. The critical data collected includes daily (Eastern Standard Time) open, close, low, and high prices and the market capacity. The findings show that the CNN-LSTM neural network model has some limitations, such as the time lag in prediction. However, it can predict prices more accurately than other deep learning methods and have an overwhelming advantage over traditional time series methods.