  In recent years, the emergence and development of third-party platforms have
greatly facilitated the growth of the Online to Offline (O2O) business.
However, the large amount of transaction data raises new challenges for
retailers, especially anomaly detection in operating conditions. Thus,
platforms begin to develop intelligent business assistants with embedded
anomaly detection methods to reduce the management burden on retailers.
Traditional time-series anomaly detection methods capture underlying patterns
from the perspectives of time and attributes, ignoring the difference between
retailers in this scenario. Besides, similar transaction patterns extracted by
the platforms can also provide guidance to individual retailers and enrich
their available information without privacy issues. In this paper, we pose an
entity-wise multivariate time-series anomaly detection problem that considers
the time-series of each unique entity. To address this challenge, we propose
GraphAD, a novel multivariate time-series anomaly detection model based on the
graph neural network. GraphAD decomposes the Key Performance Indicator (KPI)
into stable and volatility components and extracts their patterns in terms of
attributes, entities and temporal perspectives via graph neural networks. We
also construct a real-world entity-wise multivariate time-series dataset from
the business data of Ele.me. The experimental results on this dataset show that
GraphAD significantly outperforms existing anomaly detection methods.
