Auto regressive dynamic bayesian network and its application in stock market inference

Abstract

In this paper, auto regression between neighboring observed variables is added to Dynamic Bayesian Network (DBN), forming the Auto Regressive Dynamic Bayesian Network (AR-DBN). The detailed mechanism of AR-DBN is specified and inference method is proposed. We take stock market index inference as example and demonstrate the strength of AR-DBN in latent variable inference tasks. Comprehensive experiments are performed on S&P 500 index. The results show the ARDBN model is capable to infer the market index and aid the prediction of stock price fluctuation.