A method to benchmark high-dimensional process drift detection

• URL: http://arxiv.org/abs/2409.03669v2
• Date: Thu, 05 Dec 2024 18:56:04 GMT
• Title: A method to benchmark high-dimensional process drift detection
• Authors: Edgar Wolf, Tobias Windisch,
• Abstract summary: This paper studies machine learning that detect drifts in process curve datasets. A theoretic framework to synthetically generate process curves in a controlled way is introduced. An evaluation score, called the temporal area under the curve, is introduced.
• Score: 0.0
• License:
• Abstract: Process curves are multivariate finite time series data coming from manufacturing processes. This paper studies machine learning that detect drifts in process curve datasets. 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. An 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 is presented that shows that existing algorithms often struggle with datasets containing multiple drift segments.

Related papers

• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone [3.1747517745997014]
  This paper presents an automated machine learning framework designed to assist hydrologists in detecting anomalies in time series data generated by sensors in a research watershed in the northeastern United States critical zone. The framework specifically focuses on identifying peak-pattern anomalies, which may arise from sensor malfunctions or natural phenomena.
  arXiv  Detail & Related papers  (2023-09-14T19:07:50Z)
• Sequential Monte Carlo Learning for Time Series Structure Discovery [17.964180907602657]
  We present a novel structure learning algorithm that integrates sequential Monte Carlo and involutive MCMC for highly effective posterior inference. Our method can be used both in "online" settings, where new data is incorporated sequentially in time, and in "offline" settings, by using nested subsets of historical data to anneal the posterior.
  arXiv  Detail & Related papers  (2023-07-13T16:38:01Z)
• Grain and Grain Boundary Segmentation using Machine Learning with Real and Generated Datasets [0.0]
  Grain boundary segmentation using Convolutional Neural Networks (CNN) trained on a combination of real and generated data. A Voronoi tessellation pattern combined with random synthetic noise and simulated defects is developed to create a novel artificial grain image fabrication method.
  arXiv  Detail & Related papers  (2023-07-12T04:38:44Z)
• Multi-Linear Kernel Regression and Imputation in Data Manifolds [12.15802365851407]
  This paper introduces an efficient multi-linear nonparametric approximation framework for data regression and imputation, and its application to dynamic magnetic-resonance imaging (dMRI) Data features are assumed to reside in or close to a smooth manifold embedded in a kernel reproducing Hilbert space. Landmark points are identified to describe the point cloud of features by linear approximating patches which mimic the concept of tangent spaces to smooth. The multi-linear model effects dimensionality reduction, enables efficient computations, and extracts data patterns and their geometry without any training data or additional information.
  arXiv  Detail & Related papers  (2023-04-06T12:58:52Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Row-clustering of a Point Process-valued Matrix [2.0391237204597363]
  We study a matrix whose entries are marked log-Gaussian Cox processes and cluster rows of such a matrix. An efficient semi-parametric Expectation-Solution (ES) algorithm combined with functional principal component analysis (FPCA) of point processes is proposed for model estimation. The effectiveness of the proposed framework is demonstrated through simulation studies and a real data analysis.
  arXiv  Detail & Related papers  (2021-10-04T06:27:26Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Process Discovery for Structured Program Synthesis [70.29027202357385]
  A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models. We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
  arXiv  Detail & Related papers  (2020-08-13T10:33:10Z)
• Learning to predict metal deformations in hot-rolling processes [59.00006390882099]
  Hot-rolling is a metal forming process that produces a cross-section from an input through a sequence of deformations. In current practice, the rolling sequence and the geometry of their rolls are needed to achieve a given cross-section. We propose a supervised learning approach to predict a given by a set of rolls with given geometry.
  arXiv  Detail & Related papers  (2020-07-22T13:33:44Z)
• Learned Factor Graphs for Inference from Stationary Time Sequences [107.63351413549992]
  We propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data.
  arXiv  Detail & Related papers  (2020-06-05T07:06:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.