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 AR-DBN model is capable to infer the market index and aid the prediction of stock price fluctuation.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.419-428, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_36〉
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Tiehang Duan. Auto Regressive Dynamic Bayesian Network and Its Application in Stock Market Inference. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.419-428, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_36〉. 〈hal-01557633〉

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