In the era of big data, industry and public policy are able to make use of large amounts of data for policy decisions. The proliferation of cheap sensors and fast communication enables policy makers to consider complex networks as a whole, using time series data from many sources to model the system. The input/output structures of such systems are helpful in understanding how they work and designing new control laws. This article introduces the causal dynamic graph (CDG) model, which defines this structure explicitly. We provide a data-driven method for recovering the input/output structure of a CDG when every process is measured. We then discuss some of the implications of incomplete measurements on the graphical modeling and structural identification problem; we show that many relevant cases are equivalent to the simpler case where sensors are either perfect or completely missing. This will make the problem of graphically modeling such systems more tractable.

中文翻译：

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具有因果关系的数据驱动I / O结构学习

在大数据时代，行业和公共政策能够将大量数据用于决策。廉价传感器的普及和快速通信使政策制定者可以使用来自多个来源的时间序列数据对整个网络进行整体考虑，以对系统进行建模。这样的系统的输入/输出结构有助于理解它们的工作方式并设计新的控制律。本文介绍了因果动态图（CDG）模型，该模型明确定义了此结构。我们提供了一种数据驱动的方法，用于在测量每个过程时恢复CDG的输入/输出结构。然后，我们讨论不完整测量对图形建模和结构识别问题的一些影响；我们表明，许多相关情况与传感器完全或完全缺失的简单情况等效。这将使对这类系统进行图形建模的问题变得更容易处理。