khundman/telemanom
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
This tool helps operations engineers and mission controllers monitor critical systems by automatically identifying unusual patterns in sensor data. You provide historical sensor readings (telemetry) for a system, and it outputs a report highlighting specific timeframes when the system's behavior deviated from the norm, helping you proactively detect and address potential issues.
1,152 stars. No commits in the last 6 months.
Use this if you need to automatically detect anomalies in continuous, multi-stream sensor data, especially for systems like spacecraft or industrial machinery where unexpected behavior can have significant consequences.
Not ideal if your data is not time-series based, you have very little historical data, or you need to perform root-cause analysis rather than just anomaly detection.
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Jan 17, 2025
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