Auto Regressive Dynamic Bayesian Network and Its Application in Stock Market Inference

Duan, Tiehang

doi:10.1007/978-3-319-44944-9_36

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…In recent years, DBN models have seen a lot of use in industrial settings due to their characteristics as TS forecasting models and their interpretability, which has changed DBNs into more general use models. They have been applied to stock market forecasting, 3 to ecosystem changes prediction based on climate variations, 4 to topic-sentiment evolution analysis over time, 5 to assess the remaining useful life of structures, 6,7 to monitor aircraft wing cracks evolution over time 8 and to identify abnormal events during cyber security threats, 9 among others. However, in a continuous case, where Gaussianity is typically assumed, DBNs present some drawbacks: DBN models are inherently linear models, and they do not allow the insertion of discrete variables without the introduction of additional constraints.…”

Section: Introductionmentioning

confidence: 99%

Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks

Quesada

Bielza

Fontán

et al. 2022

Int J of Intelligent Sys

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When modelling multivariate continuous time series, a common issue is to find that the original processes that generated the data are nonlinear or that they drift away from the original distribution as the system evolves over time. In these scenarios, using a linear model such as a Gaussian dynamic Bayesian network (DBN) can result in severe forecasting inaccuracies due to the structure and parameters of the model remaining constant without considering either the elapsed time or the state of the system. To approach this problem, we propose a hybrid model that combines a model tree with DBNs. The model first divides the original data set into different scenarios based on the splits made by the tree over the system variables and then performs a piecewise regression in each branch of the tree to obtain nonlinear forecasts. The experimental results on three different datasets show that our model outperforms standard DBN models when dealing with nonlinear processes and is competitive with state-ofthe-art time series forecasting methods.

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