An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series
This addresses the challenge of improving anomaly detection in time series for industrial applications, though it is incremental as it builds on existing detector selection and fine-tuning approaches.
The paper tackles the problem of selecting and fine-tuning anomaly detectors for time series data, where no single detector works optimally across all anomaly types or time windows, by proposing an adaptive model (ATSDLN) that learns time series representations and selects appropriate detectors and parameters, achieving higher effectiveness and better adaptation than other methods in most cases on public datasets.
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.