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Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology

Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta, Venkataramana Runkana

TL;DR

This work introduces JHgRF-Net, a memory-augmented forecasting framework for Digital Twin environments that addresses non-stationarity and higher-order spatio-temporal dynamics by learning implicit hypergraph structures and employing dual learning pathways: Spatio-Temporal Hypergraph Convolution (STHgCN) and Spatio-Temporal Transformer Network (STTN). A gating mechanism fuses the insights from these two operators, and an uncertainty-aware variant w/Unc-JHgRF-Net provides time-varying predictive uncertainty via Gaussian likelihood optimization. Central to the approach is implicit hypergraph structure learning (HgSL/HgRL) and hypergraph-aware attention (HgAT), complemented by a transformer-based temporal-spatial encoder, enabling scalable, accurate multi-horizon forecasts on large Sensor-Tensor networks. Empirical results on twelve real-world datasets show substantial improvements over state-of-the-art baselines in RMSE/MAE/MAPE across horizons, with reliable uncertainty estimates enhancing decision-making in Digital Twin applications. The framework demonstrates strong generalization and scalability, with ablation analyses confirming the relative importance of spatial modeling and the integration of both hypergraph and transformer components. Implications include improved operational efficiency, robust forecasting under non-stationarity, and practical utility for anomaly detection and missing data handling in digital twin systems.

Abstract

Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a range of intelligent functionalities, such as modeling, simulation, and data-driven decision-making, that facilitate design optimization, performance estimation, and monitoring operations. Forecasting plays a pivotal role in Digital Twin technology, as it enables the prediction of future outcomes, supports informed decision-making, minimizes risks, driving improvements in efficiency, productivity, and cost reduction. Recently, Digital Twin technology has leveraged Graph forecasting techniques in large-scale complex sensor networks to enable accurate forecasting and simulation of diverse scenarios, fostering proactive and data-driven decision making. However, existing Graph forecasting techniques lack scalability for many real-world applications. They have limited ability to adapt to non-stationary environments, retain past knowledge, lack a mechanism to capture the higher order spatio-temporal dynamics, and estimate uncertainty in model predictions. To surmount the challenges, we introduce a hybrid architecture that enhances the hypergraph representation learning backbone by incorporating fast adaptation to new patterns and memory-based retrieval of past knowledge. This balance aims to improve the slowly-learned backbone and achieve better performance in adapting to recent changes. In addition, it models the time-varying uncertainty of multi-horizon forecasts, providing estimates of prediction uncertainty. Our forecasting architecture has been validated through ablation studies and has demonstrated promising results across multiple benchmark datasets, surpassing state-ofthe-art forecasting methods by a significant margin.

Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology

TL;DR

This work introduces JHgRF-Net, a memory-augmented forecasting framework for Digital Twin environments that addresses non-stationarity and higher-order spatio-temporal dynamics by learning implicit hypergraph structures and employing dual learning pathways: Spatio-Temporal Hypergraph Convolution (STHgCN) and Spatio-Temporal Transformer Network (STTN). A gating mechanism fuses the insights from these two operators, and an uncertainty-aware variant w/Unc-JHgRF-Net provides time-varying predictive uncertainty via Gaussian likelihood optimization. Central to the approach is implicit hypergraph structure learning (HgSL/HgRL) and hypergraph-aware attention (HgAT), complemented by a transformer-based temporal-spatial encoder, enabling scalable, accurate multi-horizon forecasts on large Sensor-Tensor networks. Empirical results on twelve real-world datasets show substantial improvements over state-of-the-art baselines in RMSE/MAE/MAPE across horizons, with reliable uncertainty estimates enhancing decision-making in Digital Twin applications. The framework demonstrates strong generalization and scalability, with ablation analyses confirming the relative importance of spatial modeling and the integration of both hypergraph and transformer components. Implications include improved operational efficiency, robust forecasting under non-stationarity, and practical utility for anomaly detection and missing data handling in digital twin systems.

Abstract

Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a range of intelligent functionalities, such as modeling, simulation, and data-driven decision-making, that facilitate design optimization, performance estimation, and monitoring operations. Forecasting plays a pivotal role in Digital Twin technology, as it enables the prediction of future outcomes, supports informed decision-making, minimizes risks, driving improvements in efficiency, productivity, and cost reduction. Recently, Digital Twin technology has leveraged Graph forecasting techniques in large-scale complex sensor networks to enable accurate forecasting and simulation of diverse scenarios, fostering proactive and data-driven decision making. However, existing Graph forecasting techniques lack scalability for many real-world applications. They have limited ability to adapt to non-stationary environments, retain past knowledge, lack a mechanism to capture the higher order spatio-temporal dynamics, and estimate uncertainty in model predictions. To surmount the challenges, we introduce a hybrid architecture that enhances the hypergraph representation learning backbone by incorporating fast adaptation to new patterns and memory-based retrieval of past knowledge. This balance aims to improve the slowly-learned backbone and achieve better performance in adapting to recent changes. In addition, it models the time-varying uncertainty of multi-horizon forecasts, providing estimates of prediction uncertainty. Our forecasting architecture has been validated through ablation studies and has demonstrated promising results across multiple benchmark datasets, surpassing state-ofthe-art forecasting methods by a significant margin.
Paper Structure (28 sections, 31 equations, 3 figures, 19 tables)

This paper contains 28 sections, 31 equations, 3 figures, 19 tables.

Table of Contents

  1. ABSTRACT
  2. Introduction
  3. Related Work on Time Series Forecasting
  4. Problem Formulation
  5. OUR APPROACH
  6. PROJECTION LAYER
  7. SPATIAL-INFERENCE
  8. Spatio-Temporal Hypergraph Convolutional Network(STHgCN)
  9. Implicit hypergraph Inferenece
  10. Hypergraph Attention Network(HgAT)
  11. Saptio-temporal Hypergraph Representation Learning
  12. Spatio-Temporal Transformer Network(STTN)
  13. Temporal Transformer
  14. Spatial Transformer
  15. Gating Mechanism
  16. ...and 13 more sections

Figures (3)

  • Figure 1: Overview of JHgRF-Net framework.
  • Figure 2: The above figure illustrates the JHgRF-Net framework, which jointly learns to incorporate a discrete dependency hypergraph structure between multiple time series variables in order to capture complex interactions. The hypergraph representation learning algorithm is used to model intrinsic spatio-temporal dependencies for multi-horizon forecasting.
  • Figure 3: The visualizations provide an overview of the model forecasts, ground-truth data, and uncertainty estimates obtained through multi-horizon forecasting on benchmark datasets.