ydup/bokeh
The interactive demo of the interpretation of the anomaly detection with Triadic Motif Fields.
This tool provides an interactive way to understand why a specific point in a time series is flagged as an anomaly. You provide a time series dataset, and it shows you how the Triadic Motif Fields method identifies unusual patterns. It's designed for researchers and practitioners working with time series data who need to interpret anomaly detection results.
No commits in the last 6 months.
Use this if you are analyzing time series data and need to visually understand the basis for an anomaly detection algorithm's conclusions.
Not ideal if you need a production-ready anomaly detection system or a tool for general-purpose time series forecasting.
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Language
Python
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Last pushed
Apr 11, 2021
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