A method to benchmark high-dimensional process drift detection

• URL: http://arxiv.org/abs/2409.03669v1
• Date: Thu, 5 Sep 2024 16:23:07 GMT
• Title: A method to benchmark high-dimensional process drift detection
• Authors: Edgar Wolf, Tobias Windisch,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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