SegTSim: A Big Data Driven Segmented Temporal Simulation Framework for Heterogeneous Multivariate Systems

Xinhang Li; Chenxi Geng; Yujia Sun

cs.LGAug 29, 2026

SegTSim: A Big Data Driven Segmented Temporal Simulation Framework for Heterogeneous Multivariate Systems

Authors: Xinhang Li, Chenxi Geng, Yujia Sun

Organizations: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

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.

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Jun 11, 2026, cs.LGJ/K move · Enter open · S save

Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation

Beinan Xu, Andy Song, Jiti Gao, Feng Liu

School of Computing Technologies, RMIT University, Melbourne, Australia · Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia · School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

Multivariate Time Series Forecasting Time Series Forecasting

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We introduce Equilibrium State Estimation (ESE), a novel paradigm for simultaneous prediction, where multiple interacting systems require separate yet coordinated forecasts. Such scenarios often arise in real-world settings such as economics and healthcare modeling. Unlike existing approaches that predict one system at a time, ESE forecasts all systems in a single pass. It first estimates the equilibrium state across systems, then generates holistic forecasts based on the difference between the current state and the estimated equilibrium. Extensive experiments on synthetic and real-world datasets, including currency exchange and COVID-19 spread modeling, demonstrate that ESE is at least as accurate as state-of-the-art (SOTA) methods while being significantly faster. In addition, ESE integrates seamlessly with conventional predictors, combining their accuracy with its exceptional efficiency and delivering a 10-70x speedup. With linear-time complexity, ESE scales far better than SOTA methods as the number of systems increases. Moreover, it remains accurate under diverse perturbations, establishing ESE as a fast, generalizable, robust, and scalable multi-prediction method.

Abstract

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