Supervised Time Series Classification for Anomaly Detection in Subsea Engineering
<|reference_start|>Supervised Time Series Classification for Anomaly Detection in Subsea Engineering: Time series classification is of significant importance in monitoring structural systems. In this work, we investigate the use of supervised machine learning classification algorithms on simulated data based on a physical system with two states: Intact and Broken. We provide a comprehensive discussion of the preprocessing of temporal data, using measures of statistical dispersion and dimension reduction techniques. We present an intuitive baseline method and discuss its efficiency. We conclude with a comparison of the various methods based on different performance metrics, showing the advantage of using machine learning techniques as a tool in decision making.<|reference_end|>
@article{çokaj2024supervised,
    title={Supervised Time Series Classification for Anomaly Detection in Subsea
  Engineering},
    author={Ergys c{C}okaj, Halvor Snersrud Gustad, Andrea Leone, Per Thomas Moe,
  Lasse Moldestad},
    journal={arXiv preprint arXiv:2403.08013},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08013},
    primaryClass={cs.LG math.DS}
}