Multivariate Time Series Anomaly Detection Using Learnable Spatial-Temporal Graph Ordinary Differential Equations Network

This paper presents MAD-ODE, a method for detecting anomalies (unusual behavior) in multivariate time series data (multiple measurements changing over time) from IoT (Internet of Things) devices using Graph Neural Networks (GNNs, which are AI models that process data organized as connected nodes and relationships). The method combines two types of graph structures—one built from prior knowledge about sensor relationships and one learned automatically—along with a special type of neural network that can capture long-range patterns in data over time.