Prediction on blockchain virtual currency transaction under long short-term memory model and deep belief network

Abstract

The advantages of blockchain virtual currency are convenient circulation, low transaction costs, and decentralized power. At present, more and more investors have focused their investment in the blockchain virtual currency. Transaction data of blockchain virtual currency belongs to the financial time series, which is noisy and random, bringing challenges to the prediction of transaction trends. The improved deep belief network (IDBN) model and Echo state network (ESN) are constructed based on deep belief network (DBN) model to explore the long short-term memory (LSTM) model and the transaction prediction of blockchain virtual currency under the DBN and to improve the transaction prediction accuracy of the blockchain virtual currency. In addition, the parameters of IDBN model were optimized using particle swarm optimization (PSO) algorithm, which are verified with the transaction data of stocks and blockchain virtual currencies (Bitcoin, Bitcoin Cash, and Ethereum), and compared with other cash algorithms for analysis. The results show that the PSO-IDBN-based time series prediction model proposed in this study can be applied to predicting the high-latitude and high-complexity data, showing superior performance compared to the traditional time series prediction and other deep learning prediction methods.