lzz19980125/awesome-multivariate-time-series-anomaly-detection-algorithms
This repository contains a reading list of papers on multivariate time series anomaly detection. This repository is still being continuously improved.
For researchers and practitioners working with complex datasets, this repository provides a curated reading list of academic papers focused on identifying unusual patterns in data streams that change over time. It helps you stay current with the latest techniques in multivariate time series anomaly detection, offering a structured overview of published research and sometimes linking directly to code implementations. This resource is valuable for data scientists, machine learning engineers, and academics who need to detect anomalies in interconnected, time-dependent data.
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Use this if you need to research or implement advanced methods for identifying unexpected behaviors or outliers across multiple related data streams over time.
Not ideal if you are looking for a ready-to-use software tool or a step-by-step guide for a specific implementation.
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Sep 11, 2025
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