Discovering and Characterizing Hidden Variables in Streaming Multivariate Time Series

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引用次数: 2

Abstract

Time series data naturally arises in many domains, such as industrial process control, robotics, finance, medicine, climatology, and numerous others. In many cases variables known to be causally relevant cannot be measured directly or the existence of such variables is unknown. This paper presents an extension of the neural network architecture, called the LO-net [1], for inferring both the existence and values of hidden variables in streaming multivariate time series, leading to deeper understanding of the domain and more accurate prediction. The core idea is to initially make predictions with one network (the observable or O net) based on a time delay embedding, following this with a gradual reduction in the temporal scope of the embedding that forces a second network (the latent or L net) to learn to approximate the value of a single hidden variable, which is then input to the O net based on the original time delay embedding. Experiments show that the architecture efficiently and accurately identifies the number of hidden variables and their values over time.
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流多元时间序列中隐变量的发现与表征
时间序列数据自然出现在许多领域,例如工业过程控制、机器人、金融、医学、气候学以及许多其他领域。在许多情况下,已知具有因果关系的变量不能直接测量,或者不知道这些变量是否存在。本文提出了一种神经网络架构的扩展,称为LO-net[1],用于推断流多元时间序列中隐藏变量的存在和值,从而更深入地理解该领域并更准确地预测。核心思想是最初基于时延嵌入对一个网络(可观察网络或O网)进行预测,随后逐渐减少嵌入的时间范围,迫使第二个网络(潜在网络或L网)学习近似单个隐藏变量的值,然后根据原始时延嵌入将其输入到O网。实验表明,该体系结构能够有效、准确地识别隐藏变量的数量及其随时间变化的值。
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