Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection
Yuanyuan Yao, Yuhan Shi, Lu Chen, Ziquan Fang, Yunjun Gao, Leong Hou U, Yushuai Li, Tianyi Li
TL;DR
Moon presents a supervised modality-conversion framework for multivariate time series anomaly detection that jointly learns from numeric time-series data and image-like MV-MTF representations. By introducing MV-MTF with a shared-parameter Multimodal-CNN and a SHAP-based anomaly explainer, Moon achieves high efficiency and robust interpretability across six real-world datasets, outperforming six state-of-the-art baselines in both accuracy and reporting quality. The framework emphasizes computational efficiency (O($n$) MV-MTF complexity) and cross-modal feature fusion, enabling scalable anomaly detection in high-dimensional settings. The resultant anomaly reports with ranked contributing variables support actionable root-cause analysis in practical deployments.
Abstract
Multivariate time series (MTS) anomaly detection identifies abnormal patterns where each timestamp contains multiple variables. Existing MTS anomaly detection methods fall into three categories: reconstruction-based, prediction-based, and classifier-based methods. However, these methods face two key challenges: (1) Unsupervised learning methods, such as reconstruction-based and prediction-based methods, rely on error thresholds, which can lead to inaccuracies; (2) Semi-supervised methods mainly model normal data and often underuse anomaly labels, limiting detection of subtle anomalies;(3) Supervised learning methods, such as classifier-based approaches, often fail to capture local relationships, incur high computational costs, and are constrained by the scarcity of labeled data. To address these limitations, we propose Moon, a supervised modality conversion-based multivariate time series anomaly detection framework. Moon enhances the efficiency and accuracy of anomaly detection while providing detailed anomaly analysis reports. First, Moon introduces a novel multivariate Markov Transition Field (MV-MTF) technique to convert numeric time series data into image representations, capturing relationships across variables and timestamps. Since numeric data retains unique patterns that cannot be fully captured by image conversion alone, Moon employs a Multimodal-CNN to integrate numeric and image data through a feature fusion model with parameter sharing, enhancing training efficiency. Finally, a SHAP-based anomaly explainer identifies key variables contributing to anomalies, improving interpretability. Extensive experiments on six real-world MTS datasets demonstrate that Moon outperforms six state-of-the-art methods by up to 93% in efficiency, 4% in accuracy and, 10.8% in interpretation performance.