MotifDisco：时间序列基序的基序因果发现
MotifDisco: Motif Causal Discovery For Time Series Motifs
AI总结：
针对时间序列基序缺乏因果发现方法的问题，提出MotifDisco框架，基于格兰杰因果性和传递熵定义基序因果性，用图神经网络学习因果关系，并在预测、异常检测和聚类中显著提升性能。
摘要：
许多时间序列，特别是健康数据流，最好被理解为一系列现象或事件的序列，我们称之为“基序”。时间序列基序是一段短小的轨迹片段，可能隐含地捕捉时间序列中的潜在现象。具体而言，我们关注从连续血糖监测仪（CGM）收集的血糖轨迹，这些轨迹内在地包含代表人类行为（如饮食和运动）的基序。识别和量化基序之间的“因果”关系的能力，可以提供一种更好地理解和表示这些模式的机制，有助于改进深度学习和生成模型，并促进先进技术的发展（例如，个性化辅导和人工胰岛素输送系统）。然而，此前没有工作开发用于时间序列基序的因果发现方法。因此，在本文中，我们开发了MotifDisco（基序因果发现），一种新颖的因果发现框架，用于从时间序列轨迹中学习基序之间的因果关系。我们形式化了“基序因果性（MC）”的概念，其灵感来自格兰杰因果性和传递熵，并开发了一个基于图神经网络的框架，通过解决无监督链接预测问题来学习基序之间的因果关系。我们将MC与预测、异常检测和聚类三个模型用例集成，以展示MC作为下游任务构建模块的用途。最后，我们在不同的健康数据流上评估我们的框架，发现基序因果性在所有用例中均提供了显著的性能提升。
英文摘要：
Many time series, particularly health data streams, can be best understood as a sequence of phenomenon or events, which we call \textit{motifs}. A time series motif is a short trace segment which may implicitly capture an underlying phenomenon within the time series. Specifically, we focus on glucose traces collected from continuous glucose monitors (CGMs), which inherently contain motifs representing underlying human behaviors such as eating and exercise. The ability to identify and quantify \textit{causal} relationships amongst motifs can provide a mechanism to better understand and represent these patterns, useful for improving deep learning and generative models and for advanced technology development (e.g., personalized coaching and artificial insulin delivery systems). However, no previous work has developed causal discovery methods for time series motifs. Therefore, in this paper we develop MotifDisco (\textbf{motif} \textbf{disco}very of causality), a novel causal discovery framework to learn causal relations amongst motifs from time series traces. We formalize a notion of \textit{Motif Causality (MC)}, inspired from Granger Causality and Transfer Entropy, and develop a Graph Neural Network-based framework that learns causality between motifs by solving an unsupervised link prediction problem. We integrate MC with three model use cases of forecasting, anomaly detection and clustering, to showcase the use of MC as a building block for downstream tasks. Finally, we evaluate our framework on different health data streams and find that Motif Causality provides a significant performance improvement in all use cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
