  With the recent advances in technology, a wide range of systems continue to
collect a large amount of data over time and thus generate time series.
Time-Series Anomaly Detection (TSAD) is an important task in various
time-series applications such as e-commerce, cybersecurity, vehicle
maintenance, and healthcare monitoring. However, this task is very challenging
as it requires considering both the intra-variable dependency and the
inter-variable dependency, where a variable can be defined as an observation in
time-series data. Recent graph-based approaches have made impressive progress
in tackling the challenges of this field. In this survey, we conduct a
comprehensive and up-to-date review of TSAD using graphs, referred to as
G-TSAD. First, we explore the significant potential of graph representation
learning for time-series data. Then, we review state-of-the-art graph anomaly
detection techniques in the context of time series and discuss their strengths
and drawbacks. Finally, we discuss the technical challenges and potential
future directions for possible improvements in this research field.
