DLI: A Deep Learning-Based Granger Causality Inference

Abstract EN

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.