Understanding Time Series Anomaly State Detection through One-Class
  Classification
Hanxu Zhou Yuan Zhang Guangjie Leng Ruofan Wang Zhi-Qin John Xu
For a long time, research on time series anomaly detection has mainly focused
on finding outliers within a given time series. Admittedly, this is consistent
with some practical problems, but in other practical application scenarios,
people are concerned about: assuming a standard time series is given, how to
judge whether another test time series deviates from the standard time series,
which is more similar to the problem discussed in one-class classification
(OCC). Therefore, in this article, we try to re-understand and define the time
series anomaly detection problem through OCC, which we call 'time series
anomaly state detection problem'. We first use stochastic processes and
hypothesis testing to strictly define the 'time series anomaly state detection
problem', and its corresponding anomalies. Then, we use the time series
classification dataset to construct an artificial dataset corresponding to the
problem. We compile 38 anomaly detection algorithms and correct some of the
algorithms to adapt to handle this problem. Finally, through a large number of
experiments, we fairly compare the actual performance of various time series
anomaly detection algorithms, providing insights and directions for future
research by researchers.