TimePred: efficient and interpretable offline change point detection for high volume data - with application to industrial process monitoring

• URL: http://arxiv.org/abs/2512.01562v1
• Date: Mon, 01 Dec 2025 11:35:48 GMT
• Title: TimePred: efficient and interpretable offline change point detection for high volume data - with application to industrial process monitoring
• Authors: Simon Leszek,
• Abstract summary: We introduce TimePred, a self-supervised framework for CPD in large-volume time series.<n>TimePred predicts each sample's normalized time index to predict mean-shift detection.<n>Experiments show competitive CPD performance while reducing computational cost by up to two orders of magnitude.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Change-point detection (CPD) in high-dimensional, large-volume time series is challenging for statistical consistency, scalability, and interpretability. We introduce TimePred, a self-supervised framework that reduces multivariate CPD to univariate mean-shift detection by predicting each sample's normalized time index. This enables efficient offline CPD using existing algorithms and supports the integration of XAI attribution methods for feature-level explanations. Our experiments show competitive CPD performance while reducing computational cost by up to two orders of magnitude. In an industrial manufacturing case study, we demonstrate improved detection accuracy and illustrate the practical value of interpretable change-point insights.

Related papers

• MuRAL-CPD: Active Learning for Multiresolution Change Point Detection [2.3711752696597435]
  Change Point Detection (CPD) is a critical task in time series analysis, aiming to identify moments when the underlying data-generating process shifts.<n>Traditional CPD methods often rely on unsupervised techniques, which lack adaptability to task-specific definitions of change.<n>We propose MuRAL-CPD, a novel semi-supervised method that integrates active learning into a multiresolution CPD algorithm.
  arXiv  Detail & Related papers  (2026-01-28T15:14:37Z)
• Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series [1.4472678336151885]
  Anomaly detection plays a vital role across a wide range of real-world domains.<n>Demand for real-time AD has surged with the rise of the (Industrial) Internet of Things.<n>We propose DAD, a novel real-time decorrelation-based anomaly detection method.
  arXiv  Detail & Related papers  (2025-07-10T08:56:40Z)
• Multiresolution Analysis and Statistical Thresholding on Dynamic Networks [49.09073800467438]
  ANIE (Adaptive Network Intensity Estimation) is a multi-resolution framework designed to automatically identify the time scales at which network structure evolves.<n>We show that ANIE adapts to the appropriate time resolution and is able to capture sharp structural changes while remaining robust to noise.
  arXiv  Detail & Related papers  (2025-06-01T22:55:55Z)
• 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)
• Cal-DETR: Calibrated Detection Transformer [67.75361289429013]
  We propose a mechanism for calibrated detection transformers (Cal-DETR), particularly for Deformable-DETR, UP-DETR and DINO. We develop an uncertainty-guided logit modulation mechanism that leverages the uncertainty to modulate the class logits. Results corroborate the effectiveness of Cal-DETR against the competing train-time methods in calibrating both in-domain and out-domain detections.
  arXiv  Detail & Related papers  (2023-11-06T22:13:10Z)
• Greedy online change point detection [0.0]
  Greedy Online Change Point Detection (GOCPD) is a computationally appealing method which finds change points by maximizing the probability of the data coming from the (temporal) concatenation of two independent models. We show that, for time series with a single change point, this objective is unimodal and thus CPD can be accelerated via ternary search with logarithmic complexity.
  arXiv  Detail & Related papers  (2023-08-14T08:59:59Z)
• Memory-free Online Change-point Detection: A Novel Neural Network Approach [22.100758943583553]
  ALACPD exploits an LSTM-autoencoder-based neural network to perform unsupervised online CPD. We show that ALACPD ranks first among state-of-the-art CPD algorithms in terms of quality of the time series segmentation.
  arXiv  Detail & Related papers  (2022-07-08T14:33:16Z)
• WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data [4.228718402877829]
  Change point detection methods have the ability to discover changes in an unsupervised fashion. We propose WATCH, a novel Wasserstein distance-based change point detection approach. An extensive evaluation shows that WATCH is capable of accurately identifying change points and outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-18T16:55:29Z)
• DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series [88.12892448747291]
  We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.
  arXiv  Detail & Related papers  (2021-09-21T10:38:26Z)
• Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation [69.34035527763916]
  Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal. We employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD.
  arXiv  Detail & Related papers  (2020-08-21T15:03:21Z)
• A Framework for Sample Efficient Interval Estimation with Control Variates [94.32811054797148]
  We consider the problem of estimating confidence intervals for the mean of a random variable. Under certain conditions, we show improved efficiency compared to existing estimation algorithms.
  arXiv  Detail & Related papers  (2020-06-18T05:42:30Z)

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.