DLI: A Deep Learning-Based Granger Causality Inference

【Author】 Peng, Wei

【Source】COMPLEXITY

【影响因子】2.121

【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.

【Keywords】

【发表时间】2020 9-Jun

【收录时间】2022-01-02

【文献类型】

【主题类别】

--