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Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series

机译：通过检测时间序列的几何结构来自适应学习具有变化结构的动态贝叶斯网络

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摘要

A dynamic Bayesian network (DBN) is one of popular approaches for relational knowledge discovery such as modeling relations or dependencies, which change over time, between variables of a dynamic system. In this paper, we propose an adaptive learning method (autoDBN) to learn DBNs with changing structures from multivariate time series. In autoDBN, segmentation of time series is achieved first through detecting geometric structures transformed from time series, and then model regions are found from the segmentation by designed finding strategies; in each found model region, a DBN model is established by existing structure learning methods; finally, model revisiting is developed to refine model regions and improve DBN models. These techniques provide a special mechanism to find accurate model regions and discover a sequence of DBNs with changing structures, which are adaptive to changing relations between multivariate time series. Experimental results on simulated and real time series show that autoDBN is very effective in finding accurate/reasonable model regions and gives lower error rates, outperforming the switching linear dynamic system method and moving window method.
机译：动态贝叶斯网络（DBN）是一种用于关系知识发现的流行方法之一，例如在动态系统变量之间随时间变化的建模关系或依赖关系。在本文中，我们提出了一种自适应学习方法（autoDBN），用于从多元时间序列中学习具有变化结构的DBN。在autoDBN中，时间序列的分割首先通过检测从时间序列转换的几何结构来实现，然后通过设计的查找策略从分割中找到模型区域；在每个发现的模型区域中，通过现有的结构学习方法建立一个DBN模型。最后，开发了模型重访以完善模型区域并改进DBN模型。这些技术提供了一种特殊的机制，可以找到准确的模型区域并发现具有变化结构的DBN序列，这些结构可以适应多元时间序列之间关系的变化。在模拟和实时序列上的实验结果表明，autoDBN在查找准确/合理的模型区域方面非常有效，并且错误率较低，优于切换线性动态系统方法和移动窗口方法。

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