Papers Change Point Detection
“Change Point Detection” 태그가 달린 논문 303편 · 필터 해제
Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation
Non-invasive continuous blood pressure (BP) monitoring using photoplethysmography (PPG) is a promising alternative to cuff-based measurements. However, existing PPG-based BP estimation studies predominantly rely on aggre…
Blood pressure estimationChange Point DetectionEvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs consistently across assets and market reg…
Change Point DetectionGeodesics of Dynamic Graphs for Regime Change Detection
Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-world applications, such as social netwo…
Change Point DetectionChange DetectionGraph RegressionDetecting Changes in Causal Dependence with Kernels and Copulas
We propose a framework for determining whether the causal dependence of an outcome $Y$ on a covariate $X$ changes at a given time point, given confounders $\boldsymbol{Z}$. For instance, in financial markets, the effect …
Change Point DetectionMultiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points
A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically identifiable. One approach embeds a sequence of network snapshots as tra…
Change Point DetectionSegmenting Human-LLM Co-authored Text via Change Point Detection
The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust. Existing detectors typically provide a binary cla…
Change Point DetectionBinary ClassificationText DetectionPi-Change: A Prior-Informed Multiple Change Point Detection Algorithm
Statistical change point (CP) detection methods typically rely on likelihood-based inference and ignore contextual information about plausible CP locations beyond the observed sequence. Although informative priors provid…
Change Point DetectionTTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust causal discovery methods that can identify intricate contemporaneous an…
Change Point DetectionMuRAL-CPD: Active Learning for Multiresolution Change Point Detection
Change Point Detection (CPD) is a critical task in time series analysis, aiming to identify moments when the underlying data-generating process shifts. Traditional CPD methods often rely on unsupervised techniques, which…
Change Point DetectionTime Series AnalysisActive LearningBehavior-Aware and Generalizable Defense Against Black-Box Adversarial Attacks for ML-Based IDS
Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as binary outputs or behavioral signals lik…
Change Point DetectionIntrusion DetectionAdversarial AttackHigh-Dimensional Change Point Detection via Graph Spanning Ratio
Inspired by graph-based methodologies, we introduce a novel graph-spanning algorithm designed to identify changes in both offline and online data across low to high dimensions. This versatile approach is applicable to Eu…
Change Point DetectionChange DetectionTimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoring
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 mu…
Change Point DetectionA Quantum Tensor Network-Based Viewpoint for Modeling and Analysis of Time Series Data
Accurate uncertainty quantification is a critical challenge in machine learning. While neural networks are highly versatile and capable of learning complex patterns, they often lack interpretability due to their ``black …
Change Point DetectionTime Series ClusteringEnhancing Hierarchical Reinforcement Learning through Change Point Detection in Time Series
Hierarchical Reinforcement Learning (HRL) enhances the scalability of decision-making in long-horizon tasks by introducing temporal abstraction through options-policies that span multiple timesteps. Despite its theoretic…
Hierarchical Reinforcement LearningChange Point DetectionImproving Anomaly Detection in Industrial Time Series: The Role of Segmentation and Heterogeneous Ensemble
Concerning machine learning, segmentation models can identify state changes within time series, facilitating the detection of transitions between normal and anomalous conditions. Specific techniques such as Change Point …
Change Point DetectionAnomaly DetectionSolve Smart, Not Often: Policy Learning for Costly MILP Re-solving
A common challenge in real-time operations is deciding whether to re-solve an optimization problem or continue using an existing solution. While modern data platforms may collect information at high frequencies, many rea…
Reinforcement LearningChange Point DetectionOn Multi-entity, Multivariate Quickest Change Point Detection
We propose a framework for online Change Point Detection (CPD) from multi-entity, multivariate time series data, motivated by applications in crowd monitoring where traditional sensing methods (e.g., video surveillance) …
Change Point DetectionAnomaly DetectionRiemannian Change Point Detection on Manifolds with Robust Centroid Estimation
Non-parametric change-point detection in streaming time series data is a long-standing challenge in signal processing. Recent advancements in statistics and machine learning have increasingly addressed this problem for d…
Change Point DetectionReal-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction
Autonomous unmanned aerial vehicles (UAVs) rely on global navigation satellite system (GNSS) pseudorange measurements for accurate real-time localization and navigation. However, this dependence exposes them to sophistic…
Change Point DetectionReinforcement Learning (RL)Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theoretical advancements and practical applica…
Change Point Detectionfeature selection