通过Kronecker时间变化图正则化对张量时间序列进行可解释性动态网络建模
Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso
AI总结：
本文提出KTVGL方法，通过Kronecker积形式建模张量时间序列，实现动态网络的可解释性建模，提升边估计精度并降低计算成本。
摘要：
随着网络服务的快速发展，各种领域如金融、医疗和在线平台都产生了大量时间序列数据。由于这些数据往往与多个变量相互作用而共同演变，估计变量之间的时间变化依赖性（即动态网络结构）对于准确建模至关重要。然而，现实数据通常以具有多个模式的张量时间序列形式表示，导致大规模、复杂的网络结构，难以解释且估计计算成本高。在本文中，我们提出了一种名为Kronecker时间变化图正则化（KTVGL）的方法，用于建模张量时间序列。我们的方法采用Kronecker积形式估计模式特定的动态网络，从而避免了过于复杂的纠缠结构，产生可解释的建模结果。此外，分块网络结构防止了随着数据维度增长而呈指数级增长的计算时间。此外，我们的方法可以扩展到流算法，使计算时间与序列长度无关。在合成数据上的实验表明，所提出的方法在边估计准确性方面优于现有方法，同时计算时间更少。为进一步展示其实际价值，我们还通过真实世界数据进行了案例研究。我们的源代码和数据集可在https://github.com/Higashiguchi-Shingo/KTVGL上获取。
英文摘要：
With the rapid development of web services, large amounts of time series data are generated and accumulated across various domains such as finance, healthcare, and online platforms. As such data often co-evolves with multiple variables interacting with each other, estimating the time-varying dependencies between variables (i.e., the dynamic network structure) has become crucial for accurate modeling. However, real-world data is often represented as tensor time series with multiple modes, resulting in large, entangled networks that are hard to interpret and computationally intensive to estimate. In this paper, we propose Kronecker Time-Varying Graphical Lasso (KTVGL), a method designed for modeling tensor time series. Our approach estimates mode-specific dynamic networks in a Kronecker product form, thereby avoiding overly complex entangled structures and producing interpretable modeling results. Moreover, the partitioned network structure prevents the exponential growth of computational time with data dimension. In addition, our method can be extended to stream algorithms, making the computational time independent of the sequence length. Experiments on synthetic data show that the proposed method achieves higher edge estimation accuracy than existing methods while requiring less computation time. To further demonstrate its practical value, we also present a case study using real-world data. Our source code and datasets are available at https://github.com/Higashiguchi-Shingo/KTVGL.
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。
