  The anomaly detection of time series is a hotspot of time series data mining.
The own characteristics of different anomaly detectors determine the abnormal
data that they are good at. There is no detector can be optimizing in all types
of anomalies. Moreover, it still has difficulties in industrial production due
to problems such as a single detector can't be optimized at different time
windows of the same time series. This paper proposes an adaptive model based on
time series characteristics and selecting appropriate detector and run-time
parameters for anomaly detection, which is called ATSDLN(Adaptive Time Series
Detector Learning Network). We take the time series as the input of the model,
and learn the time series representation through FCN. In order to realize the
adaptive selection of detectors and run-time parameters according to the input
time series, the outputs of FCN are the inputs of two sub-networks: the
detector selection network and the run-time parameters selection network. In
addition, the way that the variable layer width design of the parameter
selection sub-network and the introduction of transfer learning make the model
be with more expandability. Through experiments, it is found that ATSDLN can
select appropriate anomaly detector and run-time parameters, and have strong
expandability, which can quickly transfer. We investigate the performance of
ATSDLN in public data sets, our methods outperform other methods in most cases
with higher effect and better adaptation. We also show experimental results on
public data sets to demonstrate how model structure and transfer learning
affect the effectiveness.
