IDEAS: Universal Framework for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series is typically approached with specialized models designed to capture cross-channel dependencies, overlooking algorithms considered strictly univariate. In this work, we revisit this assumption and demonstrate that univariate detectors, when properly adapted, serve as strong and efficient alternatives to natively multivariate models. We present IDEAS (Independent Dimension Ensemble for Anomaly Scoring), a universal framework that enables the application of any anomaly detector to multivariate time series. Specifically, a detector is applied independently to each channel, and the resulting channel-wise scores are combined through a selected strategy to yield an aggregate anomaly score. To capture anomalies expressed through cross-channel relationships, the original channels are augmented with pairwise differences between highly correlated dimensions. Our experiments show that unsupervised univariate detectors adapted with IDEAS consistently outperform strong multivariate baselines spanning classical machine learning, semi-supervised deep learning, and large language models, achieving up to a 3.8$\times$ improvement in VUS-PR over the strongest baselines. Furthermore, natively multivariate detectors also improve when deployed within the IDEAS framework. Importantly, IDEAS enables detection of not only the temporal location of anomalies but also the specific channel(s) in which they occur. Finally, IDEAS can significantly accelerate computation by parallelizing a highly efficient univariate detector. These findings establish IDEAS as a highly competitive and scalable alternative to multivariate methods.