Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model

Li, Yan ¹Dai, Wei¹

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作者信息

1. Cent Univ Finance & Econ, Dept Financial Engn, Beijing, Peoples R China
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Abstract

In this study, aiming at the problem that the price of Bitcoin varies greatly and is difficult to predict, a hybrid neural network model based on convolutional neural network (CNN) and long short-term memory (LSTM) neural network is proposed. The transaction data of Bitcoin itself, as well as external information, such as macroeconomic variables and investor attention, are taken as input. Firstly, CNN is used for feature extraction. Then the feature vectors are input into LSTM for training and forecasting the short-term price of Bitcoin. The result shows that the CNN-LSTM hybrid neural network can effectively improve the accuracy of value prediction and direction prediction compared with the single structure neural network. The finding has important implications for researchers and investors in the digital currencies market.

Key words

macroeconomics/feature extraction/pricing/convolutional neural nets/cryptocurrencies/transaction processing/recurrent neural nets/economic forecasting/Bitcoin price forecasting method/CNN-LSTM hybrid neural network model/short-term price/value prediction/direction prediction/single structure neural network/convolutional neural network/long short-term memory/transaction data/macroeconomic variables/investor attention/feature extraction/feature vectors/digital currencies market

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出版年

2020

The Journal of Engineering

ISSN：

被引量4

参考文献量10

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