Anomaly detection by partitioning of multi-variate time series
This addresses anomaly detection for multivariate time series data, but appears incremental as it builds on existing anomaly detection algorithms with a partitioning step.
The authors tackled anomaly detection in multivariate time series by proposing PARADISE, a novel unsupervised partition-based method that clusters variables based on correlation coefficients to preserve inter-variable relations, resulting in significant performance improvements on synthetic and real datasets.
In this article, we suggest a novel non-supervised partition based anomaly detection method for anomaly detection in multivariate time series called PARADISE. This methodology creates a partition of the variables of the time series while ensuring that the inter-variable relations remain untouched. This partitioning relies on the clustering of multiple correlation coefficients between variables to identify subsets of variables before executing anomaly detection algorithms locally for each of those subsets. Through multiple experimentations done on both synthetic and real datasets coming from the literature, we show the relevance of our approach with a significant improvement in anomaly detection performance.