TRANSMALY: A Real-Time Telemetry Anomaly Detector Using Transformer-Based Approach

The proposed paper demonstrates a real-time anomaly detection system based on transformer architecture which handles streaming temporal data sequences. Multiple advanced components work together in the system architecture through sequential windowing mechanisms and adaptive threshold computation and distributed processing. The method incorporates transformerbased architecture with one encoder and two decoder units to process sequential data for prediction together with anomaly detection through reconstruction error. This system transforms to accommodate different data distribution patterns which results in reliable anomaly detection within multiple operation settings. The system uses parallel processing to achieve operational efficiency by operating on multiple data streams at once. The specified layout of this architecture demonstrates significant potential benefits when used for industrial applications together with IoT systems. The system outperforms classic methods for detecting temporal patterns and streaming anomalies in empirical tests because it attains enhanced accuracy and speed according to research.