Change Detection in Multivariate data streams: Online Analysis with Kernel-QuantTree

• URL: http://arxiv.org/abs/2410.13778v1
• Date: Thu, 17 Oct 2024 17:17:38 GMT
• Title: Change Detection in Multivariate data streams: Online Analysis with Kernel-QuantTree
• Abstract summary: Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA) Non-parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) histogram and the EWMA statistic. Experiments on synthetic and real-world datasets demonstrate that KQT-EWMA can control $ARL_0$ while achieving detection delays comparable to or lower than state-of-the-art methods.
• Score: 4.492556918144639
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA), a non-parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) histogram and the EWMA statistic to monitor multivariate data streams online. The resulting monitoring scheme is very flexible, since histograms can be used to model any stationary distribution, and practical, since the distribution of test statistics does not depend on the distribution of datastream in stationary conditions (non-parametric monitoring). KQT-EWMA enables controlling false alarms by operating at a pre-determined Average Run Length ($ARL_0$), which measures the expected number of stationary samples to be monitored before triggering a false alarm. The latter peculiarity is in contrast with most non-parametric change-detection tests, which rarely can control the $ARL_0$ a priori. Our experiments on synthetic and real-world datasets demonstrate that KQT-EWMA can control $ARL_0$ while achieving detection delays comparable to or lower than state-of-the-art methods designed to work in the same conditions.

Related papers

• Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control [51.92884966472683]
  MSCoT is a multi-scale, coarse-to-fine model for test-time human motion synthesis and control.<n> MSCoT discretizes motion into a multi-scale hierarchical representation and predicts the entire token sequence at each temporal scale.
  arXiv  Detail & Related papers  (2026-05-14T15:09:49Z)
• A Nonparametric Adaptive EWMA Control Chart for Binary Monitoring of Multiple Stream Processes [0.13999481573773068]
  Monitoring binomial proportions across multiple independent streams is a critical challenge in Statistical Process Control (SPC)<n>We introduce a Cumulative Standardized Binomial EWMA chart that deriving exact time-varying variance of EWMA statistic for binary multiple-stream data.<n>The CSB-EWMA chart demonstrates rapid shift detection across both ARL0 targets, with out-of-control average run length (ARL1) dropping to 3-7 samples for moderate shifts.
  arXiv  Detail & Related papers  (2026-04-13T22:11:51Z)
• Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts [51.37000123503367]
  We propose prediction-powered risk monitoring (PPRM), a semi-supervised risk-monitoring approach based on prediction-powered inference (PPI)<n>PPRM constructs anytime-valid lower bounds on the running risk by combining synthetic labels with a small set of true labels.<n>We demonstrate the effectiveness of PPRM through extensive experiments on image classification, large language model (LLM) and telecommunications monitoring tasks.
  arXiv  Detail & Related papers  (2026-02-02T15:32:14Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments [0.0]
  We present novel, interpretable, robust, and scalable DQM algorithms designed to automate anomaly detection. Our approach constructs evolving histogram templates with built-in uncertainties, featuring a statistical variant. Experiments on synthetic datasets demonstrate the high accuracy, adaptability, and interpretability of these methods.
  arXiv  Detail & Related papers  (2025-01-31T15:51:41Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• Partially-Observable Sequential Change-Point Detection for Autocorrelated Data via Upper Confidence Region [12.645304808491309]
  We propose a detection scheme called adaptive upper confidence region with state space model (AUCRSS) for sequential change point detection. A partially-observable Kalman filter algorithm is developed for online inference of SSM, and accordingly, a change point detection scheme based on a generalized likelihood ratio test is analyzed.
  arXiv  Detail & Related papers  (2024-03-30T02:32:53Z)
• 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)
• MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection [124.52227588930543]
  Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications. An inconspicuous yet powerful statistics model, the normalizing flows, is appropriate for anomaly detection and localization in an unsupervised fashion. We propose a novel Multi-Scale Flow-based framework dubbed MSFlow composed of asymmetrical parallel flows followed by a fusion flow. Our MSFlow achieves a new state-of-the-art with a detection AUORC score of up to 99.7%, localization AUCROC score of 98.8%, and PRO score of 97.1%.
  arXiv  Detail & Related papers  (2023-08-29T13:38:35Z)
• Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree [7.085868567930685]
  We introduce QT-EWMA, a nonparametric change-detection algorithm that can control the expected time before a false alarm. We show that QT-EWMA and QT-EWMA-update control the false alarm rate better than state-of-the-art methods operating in similar conditions.
  arXiv  Detail & Related papers  (2022-08-30T07:24:16Z)
• Deep Federated Anomaly Detection for Multivariate Time Series Data [93.08977495974978]
  We present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. We show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques.
  arXiv  Detail & Related papers  (2022-05-09T05:06:58Z)
• Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection [0.0]
  We present an end-to-end differentiable neural network architecture to perform anomaly detection in multivariate time series. The architecture is a cascade of dynamical systems designed to separate linearly predictable components of the signal. The anomaly detector exploits the temporal structure of the prediction residuals to detect both isolated point anomalies and set-point changes.
  arXiv  Detail & Related papers  (2022-02-25T01:50:22Z)
• Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data [6.463941665276371]
  We propose a Time-series Representational Learning through Contrastive Predictive Coding (TRL-CPC) towards anomaly detection in MVTS data. First, we jointly optimize an encoder, an auto-regressor and a non-linear transformation function to effectively learn the representations of the MVTS data sets.
  arXiv  Detail & Related papers  (2022-02-08T04:25:29Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• High-dimensional, multiscale online changepoint detection [7.502070498889449]
  We introduce a new method for high-dimensional, online changepoint detection in settings where a $p$- Gaussian data stream may undergo a change in mean. The algorithm is online in the sense that both its storage requirements and worst-case computational complexity per new observation are independent of the number of previous observations. Simulations confirm the practical effectiveness of our proposal, which is implemented in the R package 'ocd', and we also demonstrate its utility on a seismology data set.
  arXiv  Detail & Related papers  (2020-03-07T21:54:09Z)

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.