aiqbench analysis
SegTSim introduces a segmented temporal simulation framework designed to handle nonlinear dynamics in heterogeneous multivariate time-series systems, specifically addressing challenges like production relocation and exchange-rate propagation. Validated on extensive US-Japan automotive trade data, the model demonstrates robust ensemble inference capabilities that maintain low variance under significant economic perturbations. This work advances machine learning applications for complex economic forecasting by moving beyond monolithic architectures to segment-specific modeling.
SegTSim: A Big Data Driven Segmented Temporal Simulation Framework for Heterogeneous Multivariate Systems
arXiv:2609.22192v1 Announce Type: new Abstract: Heterogeneous multivariate time-series systems exhibit segment-specific nonlinear dynamics that challenge monolithic forecasting architectures. We propose SegTSim, a big-data-driven segmented temporal simulation framework that integrates segment-specific elasticity modeling with adaptive min-gating, dynamic production relocation optimization, multi-factor data fusion with exchange-rate propagation, and a deep ensemble validation pipeline. The framework is validated on US--Japan automotive trade data from USITC repositories spanning 2015 to 2025, comprising approximately 13000 annual records. Under a 25% perturbation scenario, Japanese import volume declines by 20.4% to 0.93 billion USD, while all output variables maintain coefficients of variation below 3.5% across 1000 ensemble inference runs.
Abstract:Heterogeneous multivariate time-series systems exhibit segment-specific nonlinear dynamics that challenge monolithic forecasting architectures. We propose SegTSim, a big-data-driven segmented temporal simulation framework that integrates segment-specific elasticity modeling with adaptive min-gating, dynamic production relocation optimization, multi-factor data fusion with exchange-rate propagation, and a deep ensemble validation pipeline. The framework is validated on US--Japan automotive trade data from USITC repositories spanning 2015 to 2025, comprising approximately 13000 annual records. Under a 25% perturbation scenario, Japanese import volume declines by 20.4% to 0.93 billion USD, while all output variables maintain coefficients of variation below 3.5% across 1000 ensemble inference runs.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.22192 [cs.LG]
(or arXiv:2609.22192v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.22192 Focus to learn more arXiv-issued DOI via DataCite
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