Discovering Latent Covariance Structures for Multiple Time Series
Discovering Latent Covariance Structures for Multiple Time Series
复制标题
发现多个时间序列的潜在协方差结构
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Jaesik Choi
中科院分区：
文献类型：
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作者：
Anh Tong;Jaesik Choi
分析多变量时间序列数据对于预测金融、制造和行政决策中复杂系统的未来事件和变化非常重要。成分协方差结构显著提高了高斯过程(GP)回归方法的表达能力。在本文中，我们提出了一个新的GP模型，它通过将印度自助餐过程(IBP)放在共享核的存在之上来自然地处理多个时间序列。我们的选择性协方差结构分解允许在一组多个选定的时间序列上利用共享参数。我们还考察了当引入无限隐含分量时模型的完备性。我们提出了一种实用的搜索算法，可以高效地搜索更大的结构空间。在五个真实数据集上进行的实验表明，我们的新模型在结构发现和预测性能方面优于现有方法。
Analyzing multivariate time series data is important to predict future events and changes of complex systems in finance, manufacturing, and administrative decisions. The expressiveness power of Gaussian Process (GP) regression methods has been significantly improved by compositional covariance structures. In this paper, we present a new GP model which naturally handles multiple time series by placing an Indian Buffet Process (IBP) prior on the presence of shared kernels. Our selective covariance structure decomposition allows exploiting shared parameters over a set of multiple, selected time series. We also investigate the well-definedness of the models when infinite latent components are introduced. We present a pragmatic search algorithm which explores a larger structure space efficiently. Experiments conducted on five real-world data sets demonstrate that our new model outperforms existing methods in term of structure discoveries and predictive performances.
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen