A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
<|reference_start|>A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold: The majority of modern consumer-level energy is generated by real-time smart metering systems. These frequently contain anomalies, which prevent reliable estimates of the series' evolution. This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on Mahalanobis distance and moving averages. It has been tested using real-life energy consumption data collected from smart metering systems. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system. This makes a substantial contribution by detecting unusual data movements and delivering an early warning. Early detection and subsequent troubleshooting can financially benefit organizations and consumers and prevent disasters from occurring.<|reference_end|>
@article{maitra2024a,
    title={A Real-time Anomaly Detection Using Convolutional Autoencoder with
  Dynamic Threshold},
    author={Sarit Maitra, Sukanya Kundu, Aishwarya Shankar},
    journal={arXiv preprint arXiv:2404.04311},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04311},
    primaryClass={cs.LG cs.AI}
}