RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
This addresses the need for effective and scalable anomaly detection in time series data for businesses like Alibaba, though it appears incremental as it builds on decomposition and neural network techniques.
The authors tackled the problem of time series anomaly detection by proposing RobustTAD, which integrates robust seasonal-trend decomposition with a convolutional neural network, and it performs significantly better than existing methods on public benchmark datasets.
The monitoring and management of numerous and diverse time series data at Alibaba Group calls for an effective and scalable time series anomaly detection service. In this paper, we propose RobustTAD, a Robust Time series Anomaly Detection framework by integrating robust seasonal-trend decomposition and convolutional neural network for time series data. The seasonal-trend decomposition can effectively handle complicated patterns in time series, and meanwhile significantly simplifies the architecture of the neural network, which is an encoder-decoder architecture with skip connections. This architecture can effectively capture the multi-scale information from time series, which is very useful in anomaly detection. Due to the limited labeled data in time series anomaly detection, we systematically investigate data augmentation methods in both time and frequency domains. We also introduce label-based weight and value-based weight in the loss function by utilizing the unbalanced nature of the time series anomaly detection problem. Compared with the widely used forecasting-based anomaly detection algorithms, decomposition-based algorithms, traditional statistical algorithms, as well as recent neural network based algorithms, RobustTAD performs significantly better on public benchmark datasets. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.