Modelling Networked Dynamical System by Temporal Graph Neural ODE with Irregularly Partial Observed Time-series Data
This work addresses a domain-specific challenge in fields like sensor networks or biology, where data is often incomplete and irregularly sampled, representing an incremental improvement over existing methods.
The paper tackles the problem of modeling networked dynamical systems with irregularly sampled and partially observed time-series data, proposing a method that embeds Graph Neural ODE with reliability and time-aware mechanisms to capture spatial and temporal dependencies, achieving improved performance in experiments on various systems.
Modeling the evolution of system with time-series data is a challenging and critical task in a wide range of fields, especially when the time-series data is regularly sampled and partially observable. Some methods have been proposed to estimate the hidden dynamics between intervals like Neural ODE or Exponential decay dynamic function and combine with RNN to estimate the evolution. However, it is difficult for these methods to capture the spatial and temporal dependencies existing within graph-structured time-series data and take full advantage of the available relational information to impute missing data and predict the future states. Besides, traditional RNN-based methods leverage shared RNN cell to update the hidden state which does not capture the impact of various intervals and missing state information on the reliability of estimating the hidden state. To solve this problem, in this paper, we propose a method embedding Graph Neural ODE with reliability and time-aware mechanism which can capture the spatial and temporal dependencies in irregularly sampled and partially observable time-series data to reconstruct the dynamics. Also, a loss function is designed considering the reliability of the augment data from the above proposed method to make further prediction. The proposed method has been validated in experiments of different networked dynamical systems.