Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning
This addresses automated monitoring systems with an incremental improvement over existing RNN-based methods.
The paper tackled time series anomaly detection by proposing a CNN-based segmentation approach and a transfer learning framework, achieving successful testing on multiple synthetic and real datasets.
Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a time series segmentation approach based on convolutional neural networks (CNN) for anomaly detection. Moreover, we propose a transfer learning framework that pretrains a model on a large-scale synthetic univariate time series data set and then fine-tunes its weights on small-scale, univariate or multivariate data sets with previously unseen classes of anomalies. For the multivariate case, we introduce a novel network architecture. The approach was tested on multiple synthetic and real data sets successfully.