Reduces change detection to estimation using confidence sequences.
problem Detecting changes in data streams with minimal delay and false alarms.
method Reduction from sequential change detection to sequential estimation using confidence sequences.
result Change detection scheme with minimal structural assumptions and strong guarantees.
Develops methods for inference after detecting a change in sequential data.
problem Inference after a detected change in sequential data.
method General framework for constructing confidence sets using only data up to a stopping time.
result First general method for sequential changepoint localization with theoretical guarantees.
This paper offers a distribution-free method for post-detection changepoint localization.
problem Locating the exact time of a change in distribution after a sequential detection procedure.
method A distribution-free framework using conformal test martingales for sequential change detection and post-detection inference.
result Valid post-detection coverage guarantees and non-asymptotic bounds on confidence set size.
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality properties in terms of expected detection del…
Detects changes in classifier scores to identify shifts in class priors.
problem Label shift changes in classification data.
method Sequential changepoint detection of classifier scores.
result Outperforms other detection procedures in label shift settings.
Develops a nonparametric framework for detecting changes in sequential data.
problem Detecting changes in nonparametrically specified distributions.
method Introduces e-detectors based on e-processes for nonnegative supermartingales.
result Provides bounds on average run length and detection delay.
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
problem Detecting changes in data sequences over time.
method Constructing and comparing backward and forward confidence sequences.
result The method successfully detects changes with strong nonasymptotic guarantees.
New method detects and locates changes in spatio-temporal point processes.
problem Detecting and localizing changes in spatio-temporal data.
method Score-based, likelihood-free approach estimating change time and region.
result The method provides theoretical guarantees on detection and localization accuracy.
New method calibrates false detection rates in sequential change detection.
problem Challenges in setting time-invariant thresholds for false positives.
method Simulation-based approach to time-varying thresholds.
result Accurately targets desired expected runtime while keeping false positive rate constant.
Boosts change-point detection power with optimal sub-sampling.
problem Power loss in sequential change-point detection from large history data.
method Optimal sub-sampling of history data before kernel-based detection procedures.
result Improved detection performance in extensive experiments.
Detects graph topology changes from noisy signals using prior spectral information.
problem Detecting changes in graph topology from graph signals.
method Leverages graph filtering and subspace detection to distill problem into a CUSUM-based algorithm.
result Demonstrates the effectiveness of incorporating prior spectral signatures for change-point detection.
Algorithm detects changes online using expert tracking.
problem Online change point detection in nonparametric settings.
method Sequential score function estimation and tracking the best expert approach.
result Algorithm performs well in artificial and real-world data.
We study the problem of detecting an abrupt change to the signal covariance matrix. In particular, the covariance changes from a "white" identity matrix to an unknown spiked or low-rank matrix. Two sequential change-point detection procedures are presented, based on the largest and the smallest eigenvalues of the sampl…
AUCRSS detects change points in partially observed multivariate autocorrelated data.
problem Detecting change points in multivariate autocorrelated data with limited sensing resources.
method Adaptive Upper Confidence Region (AUCRSS) with state space model (SSM), adaptive sampling policy, and generalized likelihood ratio test.
result The method outperforms existing approaches in detecting change points efficiently.
New CUSUM method detects changes in Hawkes networks efficiently.
problem Detecting abrupt changes in Hawkes networks.
method Efficient online recursive implementation of CUSUM statistic.
result Proposed CUSUM method outperforms existing methods.
This paper improves change-point detection for complex data streams using denoising score matching.
problem Timely identification of distributional shifts in high-dimensional, complex data streams.
method Score-based CUSUM change-point detection with denoising score matching.
result Denoising score matching enhances detection power by effectively controlling noise scale.
New method speeds up change-point detection in data sequences.
problem Efficiently detecting change-points in long data sequences.
method Sequential Gradient Descent and Quasi-Newton's Method.
result New method can be orders of magnitude faster than existing methods.
Detects data drift in deep learning models using neural embeddings.
problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.
Optimizes quickest change detection with bounded means under ARL constraint.
problem Quickest detection of changepoints with bounded means under ARL constraint.
method Derives universal lower and upper bounds for detection delay.
result Achieves universal lower bound in the bounded mean detection setting.
From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks. After the change, a subset of anomalous nodes which compares dissimilarly with the normal nodes. We study a sim…
We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics. The GLR statistics allow for an unknown post-change mean that represents an anomaly or novelty. We consider both fixed and time-varying projections, deri…
The paper proposes a method to detect relevant model degradations without over-alerting.
problem Detecting meaningful changes in machine learning model performance over time.
method Sequential monitoring scheme accounting for temporal dependence and multiple testing issues.
result The proposed method outperforms benchmark methods in detecting relevant changes in model quality.
In the analysis of sequential data, the detection of abrupt changes is important in predicting future changes. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified threshold…
Change detection involves segmenting sequential data such that observations in the same segment share some desired properties. Multivariate change detection continues to be a challenging problem due to the variety of ways change points can be correlated across channels and the potentially poor signal-to-noise ratio on …
NN-CUSUM detects changes in high-dimensional data using neural networks.
problem Detecting abrupt changes in high-dimensional data.
method Neural network-based CUSUM for online change-point detection.
result NN-CUSUM performs well in detecting changes in high-dimensional data.
Paper reproduces a kernel-based scan B-statistic for online change-point detection.
problem Continuous detection of distribution changes in online data streams.
method Efficient kernel-based scan B-statistic for online change-point detection.
result Scan B-statistic outperforms parametric methods in challenging scenarios.
A martingale framework for concept change detection based on testing data exchangeability was recently proposed (Ho, 2005). In this paper, we describe the proposed change-detection test based on the Doob's Maximal Inequality and show that it is an approximation of the sequential probability ratio test (SPRT). The relat…
We develop a monitoring procedure to detect changes in a large approximate factor model. Letting r be the number of common factors, we base our statistics on the fact that the (r+1)-th eigenvalue of the sample covariance matrix is bounded under the null of no change, whereas it becomes spiked under cha…
Develops methods to test nonstationarity and detect change points in RL.
problem Nonstationary environments in reinforcement learning.
method Consistent testing procedure and change point detection method.
result Validates the effectiveness of the proposed methods in real data.
A new framework detects forecast model inadequacies using online monitoring of forecast errors.
problem Inaccurate forecasts lead to poor decision-making in complex models.
method Sequential changepoint techniques on forecast errors for real-time identification of process changes.
result The framework identifies shifts in forecast errors faster than in the original models, indicating process changes.
PERCEPT detects changes in high-dimensional data streams using topological data analysis.
problem Detecting changes in high-dimensional data streams, especially when embedded in a low-dimensional space.
method Leverages topological data analysis to learn embedded topology as a point cloud via persistence diagrams, then applies non-parametric monitoring for detecting changes.
result Demonstrates efficient detection of online changes from high-dimensional data streams.
Graph change-point detection method learns graph similarity from data.
problem Detect abrupt changes in dynamic networks.
method Siamese graph neural network for graph similarity learning.
result Method detects changes in diverse types of networks with minimal data history.
We consider online detection strategies for identifying a change point in a stream of quantum particles allegedly prepared in identical states. We show that the identification of the change point can be done without error via sequential local measurements while attaining the optimal performance bound set by quantum mec…
When applying principal component analysis (PCA) for dimension reduction, the most varying projections are usually used in order to retain most of the information. For the purpose of anomaly and change detection, however, the least varying projections are often the most important ones. In this article, we present a nov…
Optimal search for change point anomaly in multiple processes.
problem Detecting a change point in an anomalous process among multiple normal processes.
method Deterministic search algorithm balancing sample complexity and detection accuracy.
result Asymptotically optimal in minimizing Bayes risk.
New method detects text changes under dependencies, outperforming baselines.
problem Detecting structural changes in m-dependent text data. method Kernel change-point detection under m-dependence. result Consistent and weakly consistent detection of change points in m-dependent text. New test detects local changes in high-dimensional Gaussian graphical models online.
problem Detecting abrupt changes in high-dimensional Gaussian graphical models in real-time.
method Develops a novel test based on the ℓ∞ norm of the normalized covariance matrix.
result Demonstrates good performance in various experimental settings.
A flexible nonparametric online changepoint detection algorithm for high-frequency data.
problem Detecting changes in real-time in high-frequency data streams with limited computational resources.
method NP-FOCuS, a sequential likelihood ratio test for a change in the empirical cumulative density function, using functional pruning.
result NP-FOCuS outperforms current nonparametric online changepoint techniques in various settings.
Model change points in time-series data with neural SDEs and variational autoencoders.
problem Modeling change points in time-series data with neural stochastic differential equations.
method Proposes a novel model formulation and training procedure based on the variational autoencoder framework, alternating between updating neural SDE parameters and change points.
result Demonstrates the expressive power of the proposed model in modeling both classical parametric SDEs and real datasets with distribution shifts.
Paper proposes semi-supervised learning using change points for sequence classification.
problem Limited labeled data for sequential sensor data classification.
method Change point detection for identifying class changes, semi-supervised learning with labeled and unlabeled data.
result Improved classification performance on human activity recognition datasets.
A major impact of globalization has been the information flow across the financial markets rendering them vulnerable to financial contagion. Research has focused on network analysis techniques to understand the extent and nature of such information flow. It is now an established fact that a stock market crash in one co…
Online change detection algorithm using random Fourier features.
problem Online non-parametric change point detection in multivariate data streams.
method Sequential testing procedure based on random Fourier features.
result The algorithm has optimal detection delay in the minimax sense.
Detecting emergence of a low-rank signal from high-dimensional data is an important problem arising from many applications such as camera surveillance and swarm monitoring using sensors. We consider a procedure based on the largest eigenvalue of the sample covariance matrix over a sliding window to detect the change. T…
A new algorithm detects changes in high-dimensional data efficiently under sampling constraints.
problem Real-time monitoring of high-dimensional streaming data with limited sampling.
method Incorporates multi-armed bandit approaches into sequential change-point detection.
result Proposes TSSRP algorithm for efficient detection of changes.
Novel method improves load estimation in power grids using anomaly and change point detection.
problem Improving load estimation in power grid systems.
method Combining unsupervised anomaly and change point detection methods for automatic filtering.
result Automatic load estimation is accurate with 90% estimates within a 10% error margin.
Detects data drift and outliers affecting ML model performance over time.
problem Detecting distribution changes between training and deployment datasets for machine learning models.
method Nonparametrically tests model prediction confidence distributions for changes using Change Point Models (CPMs). Also uses nonparametric outlier methods.
result Demonstrates robustness of the method under various levels of drift class contamination.
We propose a general formalism of iterated random functions with semigroup property, under which exact and approximate Bayesian posterior updates can be viewed as specific instances. A convergence theory for iterated random functions is presented. As an application of the general theory we analyze convergence behaviors…
This paper considers a statistical signal processing problem involving agent based models of financial markets which at a micro-level are driven by socially aware and risk- averse trading agents. These agents trade (buy or sell) stocks by exploiting information about the decisions of previous agents (social learning) v…