- Single-Feature Versus Multi-Feature Processing
- Single-Feature Processing
- Multi-Feature Processing (Sensor Fusion)
- Configuration Parameters
Amber Input Data Recommendations
- Confounding Features
- Redundant and Poorly Correlated Features
- Missing Data and Variable Sample Rates
- Categorical Data
Amber Training Recommendations
- The Autotuning Buffer
- The Training Buffer
- Anomalies During Training
- Using Pretraining
- Re-enabling Learning
- (ID) Cluster ID
- (SI) Smoothed Anomaly Index
- (RI) Raw Anomaly Index
- (AD) Anomaly Detections
- (AH) Anomaly History
- (AM) Anomaly Metric
- (AW) Amber Warning Level
- (RC) Root Cause Analysis
Examples
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