  The detection of anomalies in multivariate time series data is crucial for
various practical applications, including smart power grids, traffic flow
forecasting, and industrial process control. However, real-world time series
data is usually not well-structured, posting significant challenges to existing
approaches: (1) The existence of missing values in multivariate time series
data along variable and time dimensions hinders the effective modeling of
interwoven spatial and temporal dependencies, resulting in important patterns
being overlooked during model training; (2) Anomaly scoring with
irregularly-sampled observations is less explored, making it difficult to use
existing detectors for multivariate series without fully-observed values. In
this work, we introduce a novel framework called GST-Pro, which utilizes a
graph spatiotemporal process and anomaly scorer to tackle the aforementioned
challenges in detecting anomalies on irregularly-sampled multivariate time
series. Our approach comprises two main components. First, we propose a graph
spatiotemporal process based on neural controlled differential equations. This
process enables effective modeling of multivariate time series from both
spatial and temporal perspectives, even when the data contains missing values.
Second, we present a novel distribution-based anomaly scoring mechanism that
alleviates the reliance on complete uniform observations. By analyzing the
predictions of the graph spatiotemporal process, our approach allows anomalies
to be easily detected. Our experimental results show that the GST-Pro method
can effectively detect anomalies in time series data and outperforms
state-of-the-art methods, regardless of whether there are missing values
present in the data. Our code is available: https://github.com/huankoh/GST-Pro.
