Detailed Description
The uplink anomaly detection LSTM autoencoder model leverages recurrent neural networks to analyze 5G uplink traffic patterns and detect per-KPI deviations in real time. By learning temporal dependencies in uplink KPIs, the model identifies abnormal behaviors such as interference, degradation, or unexpected traffic dynamics. Designed for integration within telecom monitoring pipelines, the model supports proactive fault detection and improves network reliability by enabling early identification of potential issues before they escalate into service-impacting failures.
Key Features
- Real-time uplink anomaly detection based on time-series KPIs
- LSTM-based temporal modeling of 5G network behavior
- Scalable to large-scale network datasets
- Native integration with VictoriaMetrics for metric ingestion
Use Cases
- Predictive maintenance and early fault detection
- Performance optimization and fault diagnosis
