analyze temporal dynamics

Irregular and heterogeneous longitudinal biometric trajectories in electronic health records (EHRs) pose challenges for robust phenotyping and cross-institutional analysis. Method: We propose a cross-platform analytical framework integrating thin-plate spline regression for asynchronous time-series smoothing with k-means clustering for clinical phenotype identification. The method ensures algorithmic consistency and reproducibility across R and SAS implementations, augmented by multi-core parallelization, joint evaluation using the Adjusted Rand Index and silhouette coefficient, and flexible parameter tuning with integrated visualization. Results: Validation on simulated blood pressure data demonstrates high inter-platform clustering concordance (Adjusted Rand Index > 0.95), substantially enhancing modeling robustness for irregular longitudinal data and compatibility across heterogeneous EHR systems. This framework provides a generalizable, scalable, and reproducible technical foundation for multi-platform EHR phenotyping studies.

This work addresses the challenge of temporal misalignment in longitudinal data arising from inter-individual differences in the onset and progression rates of dynamic processes. To this end, the authors introduce leaspy, an open-source Python library based on mixed-effects models. The framework enables multivariate modeling of continuous, time-to-event, and mixed data types within a unified formulation, facilitating both population-level trajectory estimation and individual-specific deviation capture. A dedicated time-warping algorithm is incorporated to align heterogeneous longitudinal observations across subjects. Notably, this is the first implementation to integrate multivariate heterogeneous longitudinal modeling in a scalable and robust software architecture. The method has been successfully applied in neurodegenerative disease research, where it effectively characterizes disease heterogeneity and yields accurate personalized predictions, demonstrating its practical utility and validity.

This study addresses the challenge of directly modeling spatiotemporal human behavior from high-frequency, timestamped smartphone positioning data. We propose a probabilistic trajectory reconstruction framework inspired by binning-based approaches, integrating time-series filtering with joint spatiotemporal trajectory modeling and employing particle Gibbs sampling for Bayesian smoothing and latent trajectory inference. Unlike conventional binning methods—which discretize space and time coarsely—our approach preserves temporal continuity and spatial fidelity while robustly handling measurement noise and irregular sampling intervals. Empirical evaluation demonstrates substantial improvements in trajectory estimation accuracy, particularly under realistic conditions characterized by high noise and heterogeneous sampling rates. The method has been validated in adolescent health and behavioral research, successfully supporting individual-level activity pattern modeling. Its principled, scalable design positions it as a promising standardized preprocessing tool for mobile tracking studies.

Longitudinal omics data pose significant challenges for dynamic modeling and clinical translation due to their high dimensionality, temporal imbalance, and non-Gaussian distributional properties. To address these challenges, this study rigorously delineates the theoretical boundaries between time-series and longitudinal analysis, and establishes a methodology classification framework specifically tailored to omics characteristics—encompassing single-cell longitudinal modeling, multi-omics integration, network dynamics, and FDA-compliant analysis. We systematically unify linear and generalized linear mixed models, functional data analysis, Bayesian hierarchical modeling, survival analysis, and multi-view cross-platform fusion algorithms. The resulting methodological guide comprehensively addresses modeling assumptions, algorithmic suitability, and software implementation, delivering a reproducible, scalable analytical framework. This work substantially enhances the rigor, interpretability, and translational utility of complex longitudinal omics studies.

Estimating the rate of change in nonlinear trajectories under individually scheduled, unequally spaced longitudinal measurements remains challenging—existing models struggle to jointly estimate dynamic change parameters and theory-driven substantive parameters. To address this, we propose a novel framework that conceptualizes the rate of change as the area under a time-varying functional curve, approximating the average rate within each interval by the instantaneous rate at its midpoint. This enables simultaneous estimation of both change and substantive parameters. The method is implemented within a latent-variable structural equation modeling framework using OpenMx or Mplus 8, integrating numerical integration with interval-specific approximations. Simulation and empirical studies demonstrate high accuracy, robustness, and the ability to derive both baseline-level and interval-specific change metrics. Accompanying open-source code ensures flexibility and reproducibility. The approach substantially enhances theoretical interpretability and practical utility for modeling nonlinear longitudinal processes.

This work addresses the challenge of uncovering intrinsic patterns underlying behavioral coordination and dynamic change in high-dimensional, noisy, and temporally complex human pose data. To this end, the authors propose a general-purpose analytical framework that integrates systematic preprocessing, flexible dimensionality reduction—accommodating both linear and nonlinear methods—and temporal recurrence analysis. The framework is designed to handle diverse pose data modalities, including facial or full-body, 2D or 3D, and single- or multi-person configurations. It enables unified modeling of pose dynamics across varied experimental contexts and demonstrates strong effectiveness and generalizability in extracting theoretically interpretable movement patterns, as validated through three empirical case studies.

This study addresses the structural distortions introduced when linearizing two-dimensional geospatial data into one-dimensional orderings, which often produce misleading visual artifacts that obscure genuine spatiotemporal patterns. To mitigate this issue, the authors propose a metric-driven visual analytics approach that uniquely integrates neighborhood-preserving metrics with visual enhancement techniques—specifically glyphs, halos, and stippling—to interactively and interpretable identify and annotate linearization artifacts through a dedicated interface. By coupling quantitative fidelity measures with perceptually effective visual encodings, the method significantly enhances analysts’ ability to distinguish authentic spatial structures from distortion-induced artifacts. The efficacy of the proposed framework is demonstrated through a case study on COVID-19 incidence rates in Germany, where it successfully supports accurate pattern recognition amidst complex spatial data.

This work proposes an interactive visual analytics approach to address the challenges of visual clutter and redundancy in large-scale time series visualization, which often obscure critical trends. By integrating M4 sampling, dynamic time warping (DTW) similarity computation, and a greedy selection strategy, the method automatically identifies a representative subset of time series that preserves essential patterns while minimizing redundancy. A coordinated multi-view visualization framework further enables users to efficiently explore and interpret the data. The proposed technique significantly enhances visual clarity, interpretability, and analytical efficiency without sacrificing the core temporal characteristics of the original dataset.