datamllab/tods
TODS: An Automated Time-series Outlier Detection System
Detecting unusual patterns or events in real-time data streams is crucial for many professionals. This system helps identify anomalies in time-series data, like sensor readings or financial transactions. You input your time-series data, and it outputs alerts or flags indicating suspicious points, patterns, or system-wide deviations. It's designed for data analysts, operations engineers, or fraud prevention specialists who need to quickly pinpoint abnormal behavior without deep machine learning expertise.
1,654 stars. No commits in the last 6 months.
Use this if you need an automated way to detect unusual or unexpected behavior in continuous streams of data, such as identifying a malfunctioning machine, a fraudulent transaction, or an abnormal network event.
Not ideal if your data is static, not time-based, or if you need to perform general classification or regression tasks rather than anomaly detection.
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1,654
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205
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 11, 2023
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