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

TL;DR

This paper introduces a hybrid hypergraph forecasting architecture with memory augmentation and uncertainty modeling to improve Digital Twin predictions, especially in non-stationary environments, outperforming existing methods.

Contribution

It proposes a novel hybrid hypergraph model with memory and uncertainty estimation, enhancing scalability and adaptability in Digital Twin forecasting tasks.

Findings

Outperforms state-of-the-art forecasting methods on benchmark datasets

Demonstrates improved adaptation to non-stationary environments

Provides reliable multi-horizon uncertainty estimates

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.