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制作时间:为横截面数据的时序分析的伪时间序列

Making Time: Pseudo Time-Series for the Temporal Analysis of Cross Section Data
课程网址: http://videolectures.net/ida07_tucker_mt/  
主讲教师: Allan Tucker
开课单位: 布鲁内尔大学
开课时间: 2007-10-08
课程语种: 英语
中文简介:
许多生物和医学过程(例如疾病和发展)的进展本质上是暂时的。然而,与此类过程相关联的许多数据集来自横截面研究,这意味着它们提供了跨群体的特定过程的快照,但实际上并未包含任何时间信息。在本文中,我们通过使用加权图的最小生成树方法构建横截面数据样本的时间排序来解决这个问题。我们将这些重构的排序称为伪时间序列,并将它们合并到诸如动态贝叶斯网络的时间模型中。我们的初步研究结果表明,包括伪时间信息可以提高分类性能。最后,我们概述了本研究的未来发展方向,包括考虑时间序列构建的不同方法和其他时间建模方法。
课程简介: The progression of many biological and medical processes such as disease and development are inherently temporal in nature. However many datasets associated with such processes are from cross-section studies, meaning they provide a snapshot of a particular process across a population, but do not actually contain any temporal information. In this paper we address this by constructing temporal orderings of cross-section data samples using minimum spanning tree methods for weighted graphs. We call these reconstructed orderings pseudo time-series and incorporate them into temporal models such as dynamic Bayesian networks. Results from our preliminary study show that including pseudo temporal information improves classification performance. We conclude by outlining future directions for this research, including considering different methods for time-series construction and other temporal modelling approaches.
关 键 词: 横截面的研究; 最小生成树; 伪时间序列
课程来源: 视频讲座网
最后编审: 2020-10-01:yumf
阅读次数: 59