Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection
This work addresses anomaly detection in multivariate time-series data for industries like manufacturing or monitoring, but it appears incremental as it builds on existing auto-encoder and ensemble methods.
The authors tackled the problem of multivariate time-series anomaly detection by proposing RAE-MEPC, an unsupervised model that uses multi-resolution ensemble and predictive coding to learn normal representations, and it outperformed benchmark models on real-world datasets.
As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential role in diverse industries. It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data. However, multivariate time-series anomaly detection is challenging because real-world time-series data exhibit complex temporal dependencies. For this task, it is crucial to learn a rich representation that effectively contains the nonlinear temporal dynamics of normal behavior. In this study, we propose an unsupervised multivariate time-series anomaly detection model named RAE-MEPC which learns informative normal representations based on multi-resolution ensemble and predictive coding. We introduce multi-resolution ensemble encoding to capture the multi-scale dependency from the input time series. The encoder hierarchically aggregates the temporal features extracted from the sub-encoders with different encoding lengths. From these encoded features, the reconstruction decoder reconstructs the input time series based on multi-resolution ensemble decoding where lower-resolution information helps to decode sub-decoders with higher-resolution outputs. Predictive coding is further introduced to encourage the model to learn the temporal dependencies of the time series. Experiments on real-world benchmark datasets show that the proposed model outperforms the benchmark models for multivariate time-series anomaly detection.