A method to benchmark high-dimensional process drift detection
<|reference_start|>A method to benchmark high-dimensional process drift detection: Process curves are multi-variate finite time series data coming from manufacturing processes. This paper studies machine learning methods for drifts of process curves. A theoretic framework to synthetically generate process curves in a controlled way is introduced in order to benchmark machine learning algorithms for process drift detection. A evaluation score, called the temporal area under the curve, is introduced, which allows to quantify how well machine learning models unveil curves belonging to drift segments. Finally, a benchmark study comparing popular machine learning approaches on synthetic data generated with the introduced framework shown.<|reference_end|>
@article{wolf2024a,
    title={A method to benchmark high-dimensional process drift detection},
    author={Edgar Wolf and Tobias Windisch},
    journal={arXiv preprint arXiv:2409.03669},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03669},
    primaryClass={stat.ML cs.AI cs.LG}
}