ChunjingXiao/DiffAD
Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models, KDD 2023
This project helps operations engineers and data analysts automatically spot unusual patterns in critical system data, like server metrics or satellite readings. You provide historical time-series data from sensors or logs, and it highlights moments or periods that deviate significantly from normal behavior. This tool is designed for professionals monitoring the health and performance of complex systems.
120 stars. No commits in the last 6 months.
Use this if you need to reliably detect anomalies in multivariate time-series data from sources like IT infrastructure, industrial control systems, or spacecraft telemetry.
Not ideal if your data is not time-series based, if you need real-time anomaly detection with extremely low latency, or if your dataset is very small and lacks historical patterns.
Stars
120
Forks
15
Language
Python
License
—
Category
Last pushed
Sep 06, 2023
Commits (30d)
0
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