khundman/telemanom

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1187 stars · 267 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Telemanom is a Python framework that uses vanilla LSTMs (Keras/TensorFlow) to detect anomalies in multivariate time series sensor data, with nonparametric dynamic thresholding of prediction errors. It includes the spacecraft telemetry datasets and experiments from the NASA SMAP and Mars Science Laboratory missions from a 2018 KDD paper.

Use cases

• detect anomalies in multivariate sensor telemetry
• reproduce the KDD 2018 spacecraft anomaly detection experiments
• train LSTM models on normal system behavior and flag deviations
• benchmark anomaly detection on the SMAP and MSL labeled datasets
• apply unsupervised dynamic thresholding to time series prediction errors
• monitor IoT or industrial sensor streams for unexpected behavior

When to choose

• you need LSTM-based anomaly detection for multivariate time series
• you want a research-proven baseline with labeled spacecraft anomaly data
• you need unsupervised, nonparametric error thresholding without labeled anomalies

When to avoid

• you need real-time streaming detection at scale in production
• you want transformer or modern architecture approaches
• you need actively maintained tooling with current TensorFlow versions

Facets

framework · maturity maintenance

machine-learning deep-learning data-science analytics machine-learning time-series data-science artificial-intelligence python cross-platform lstm anomaly-detection time-series keras tensorflow telemetry nasa kdd2018 sensor-data docker

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