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Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different time-series explicitly, resulting in inevitable false alarms. In this paper, we propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue. Our framework considers each univariate time-series as an individual feature and includes two graph attention layers in parallel to learn the complex dependencies of multivariate time-series in both temporal and feature dimensions. In addition, our approach jointly optimizes a forecasting-based model and are construction-based model, obtaining better time-series representations through a combination of single-timestamp prediction and reconstruction of the entire time-series. We demonstrate the efficacy of our model through extensive experiments. The proposed method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method has good interpretability and is useful for anomaly diagnosis.","url_abs":"https://arxiv.org/abs/2009.02040v1","url_pdf":"https://arxiv.org/pdf/2009.02040v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multivariate-time-series-anomaly-detection","repo_url":"https://github.com/ML4ITS/mtad-gat-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multivariate-time-series-anomaly-detection","repo_url":"https://github.com/mangushev/mtad-gat","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"graph-attention","task_name":"Graph Attention"},{"task_slug":"spatio-temporal-forecasting","task_name":"Spatio-Temporal Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-anomaly-detection","task_name":"Time Series Anomaly Detection"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"}],"methods":[{"method_slug":"gat","method_name":"GAT"},{"method_slug":"graph-self-attention","method_name":"Graph Self-Attention"},{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-anomaly-detection-on-smap","task":"Unsupervised Anomaly Detection","dataset":"SMAP","model":"MTAD-GAT","rank_in_archive_order":4,"of":9,"metrics":{"AUC":"98.44","F1":"88.80","Precision":"79.91","Recall":"99.91"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2009.02040","atlas_url":"https://app.syntology.ai/?focus=2009.02040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.02040"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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