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drift-detection
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Detect IoT Telemetry Anomalies
iot-anomalies
ruvnet/ruflo
367
This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.
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Anomaly Detection for Trading Signals
trader-signal
ruvnet/ruflo
281
This tool generates sophisticated trading signals by leveraging an advanced anomaly detection engine. It scans financial assets for various market anomalies—such as spikes, drifts, and oscillations—and classifies them. It then integrates neural network prediction and searches historical pattern databases to output highly ranked, actionable trading signals, complete with confidence scores and target parameters.
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