DLI: A Deep Learning-Based Granger Causality Inference

Wei Peng

Complexity, 2020, vol. 2020, 1-6

Abstract:

Integrating autoencoder (AE), long short-term memory (LSTM), and convolutional neural network (CNN), we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference (DLI). Two contributions of the proposed DLI are to reveal the Granger causality between the bitcoin price and S&P index and to forecast the bitcoin price and S&P index with a higher accuracy. Experimental results demonstrate that there is a bidirectional but asymmetric Granger causality between the bitcoin price and S&P index. And the DLI performs a superior prediction accuracy by integrating variables that have causalities with the target variable into the prediction process.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5960171

DOI: 10.1155/2020/5960171

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