akash13singh/lstm_anomaly_thesis
Anomaly detection for temporal data using LSTMs
This project helps you identify unusual patterns or events within continuous streams of data, like sensor readings or usage logs, even without prior examples of what an 'anomaly' looks like. It takes your historical time series data and outputs scores that highlight points in time that deviate significantly from learned normal behavior. This is ideal for analysts, operations managers, or scientists who monitor equipment, systems, or natural phenomena and need to flag irregular occurrences.
226 stars. No commits in the last 6 months.
Use this if you have continuous, sequential data and need to automatically detect deviations or outliers without having pre-labeled examples of anomalies.
Not ideal if your data isn't sequential, if you already have a comprehensive set of labeled anomalies for training, or if you're dealing with very simple time series where basic methods might suffice.
Stars
226
Forks
85
Language
Jupyter Notebook
License
MIT
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Last pushed
Oct 05, 2021
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