Latent Dynamic Networked System Identification with High-Dimensional Networked Data

TL;DR

This paper introduces a new algorithm for identifying latent dynamic networked systems from high-dimensional data, leveraging network structure and dimension reduction to improve system understanding and analysis.

Contribution

It presents a novel method that uses dynamic latent variables with auto-regressive models to identify complex networked systems from high-dimensional sensor data.

Findings

Effective in capturing predictable latent variables

Demonstrated on industrial process network data

Improves system identification accuracy

Abstract

Networked dynamic systems are ubiquitous in various domains, such as industrial processes, social networks, and biological systems. These systems produce high-dimensional data that reflect the complex interactions among the network nodes with rich sensor measurements. In this paper, we propose a novel algorithm for latent dynamic networked system identification that leverages the network structure and performs dimension reduction for each node via dynamic latent variables (DLVs). The algorithm assumes that the DLVs of each node have an auto-regressive model with exogenous input and interactions from other nodes. The DLVs of each node are extracted to capture the most predictable latent variables in the high dimensional data, while the residual factors are not predictable. The advantage of the proposed framework is demonstrated on an industrial process network for system identification and dynamic data analytics.