This work introduces a novel framework for dynamic factor model-based data integration of multiple subjects time series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes inter-subject differences between two pre-labeled groups by considering a combination of group spatial information and individual temporal dependence. Furthermore, it enables the identification of intra-subject differences over time by employing different model configurations for each subject. Methodologically, the framework combines a novel principal angle-based rank selection algorithm and a non-iterative integrative analysis framework. Inspired by simultaneous component analysis, this approach also reconstructs identifiable latent factor series with flexible covariance structures. The performance of the GRIDY models is evaluated through simulations conducted under various scenarios. An application is also presented to compare resting-state functional MRI data collected from multiple subjects in the Autism Spectrum Disorder and control groups.

翻译：本文提出了一种基于动态因子模型的多个体时间序列数据整合新框架，称为群体整合性动态因子（GRIDY）模型。该框架通过结合群体空间信息与个体时间依赖性，识别并刻画两个预先标记群体间的个体差异。此外，通过为每个个体采用不同的模型配置，该框架还能够识别个体内随时间变化的内在差异。方法论上，该框架融合了新颖的基于主成分角的秩选择算法与非迭代整合分析框架。受同步成分分析启发，该方法还重构了具有灵活协方差结构的可识别潜在因子序列。通过多种场景下的模拟实验评估了GRIDY模型的性能，并展示了其在比较自闭症谱系障碍组与对照组多个体静息态功能磁共振成像数据中的应用实例。