Sequential Change Detection Research Articles

Ordered spatial configuration protects representations of dissimilar items and reduces the similarity effect in visual working memory.

Although similarity could improve visual working memory (VWM) performance, it remains unclear how the spatial configuration of visual information influences the similarity effect in VWM. We explored this question by manipulating the orderliness of spatial configuration (ordered vs. scrambled) in the simultaneous (Experiment 1) and sequential (Experiment 2) change detection tasks. The results showed that similarity improved VWM performance when memory items were presented simultaneously and sequentially. For the simultaneous memory array containing similar and dissimilar items, the performance of the ordered spatial configuration was better than that of the scrambled spatial configuration when probing dissimilar items, while no such difference was found when probing similar items. Further, the similarity effect value in the scrambled spatial configuration was higher than that in the ordered spatial configuration. For the sequential memory array containing similar and dissimilar items, spatial configuration did not affect the similarity effect in VWM. Taken together, these findings suggest that spatial configuration could modulate the similarity effect when memory items are presented simultaneously, in which the ordered spatial configuration protects representations of dissimilar items and reduces the similarity effect in VWM. Our study provides additional evidence for the role of spatial configuration in the similarity effect in VWM, and supports the hierarchical model.

Thompson Sampling-Based Partially Observable Online Change Detection for Exponential Families

This paper proposes a holistic sequential change detection framework for partially observable high-dimensional data streams with exponential-family distributions. The framework first proposes a general composite decomposition for exponential-family distributed data by projecting its natural parameter onto normal bases and abnormal bases, which enables efficient inference for sparse changes. Then, the inference results are used for detection scheme construction, and different types of test statistics can be compacted in our framework. Last, by further designing the test statistic as the reward function in the combinatorial multi-armed bandit problem, a Thompson sampling-based sensor allocation strategy is constructed to select the most anomalous variables. Theoretical properties of the detection framework are discussed. Finally, examples of Gaussian, Poisson, and binomial distributed data streams are given in numerical studies and case studies to evaluate the performance of our proposed method. History: Bianca Maria Colosimo served as the senior editor. Funding: C. Zhang is partially supported by the NSFC [Grants 71932006 and 72271138], the BNSF [Grant 9222014], and the ASFC [Grant 2020Z063058001]. H. Yan is partially supported by NIH [R21 AI157618] and NSF [CMMI 2316654]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2022.00011 . Data Ethics & Reproducibility Note: No data ethics considerations are foreseen related to this paper. The code capsule is available on Code Ocean at https://codeocean.com/capsule/8794940/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.00011 ).

Round Robin Active Sequential Change Detection for Dependent Multi-Channel Data

Round Robin Active Sequential Change Detection for Dependent Multi-Channel Data

Data-adaptive symmetric CUSUM for sequential change detection

Detecting change points sequentially in a streaming setting, especially when both the mean and the variance of the signal can change, is often a challenging task. A key difficulty in this context often involves setting an appropriate detection threshold, which for many standard change statistics may need to be tuned depending on the prechange and postchange distributions. This presents a challenge in a sequential change detection setting when a signal switches between multiple distributions. Unfortunately, change point detection schemes that use the log-likelihood ratio, such as cumulative sum (CUSUM) and the generalized log-likelihood ratio (GLR), are quick to react to changes but are not symmetric when both the mean and the variance of the signal change. This makes it difficult to set a single threshold to detect multiple change points sequentially in a streaming setting. We propose a modified version of CUSUM that we call data-adaptive symmetric CUSUM (DAS-CUSUM). The DAS-CUSUM procedure is symmetric for changes between distributions, making it suitable to set a single threshold to detect multiple change points sequentially in a streaming setting. We provide results that relate the expected detection delay and average run length for our proposed procedure when both prechange and postchange distributions are normally distributed. Experiments on simulated and real-world data show the utility of DAS-CUSUM.

A note on online change point detection

We investigate sequential change point estimation and detection in univariate nonparametric settings, where a stream of independent observations from sub-Gaussian distributions with a common variance factor and piecewise-constant but otherwise unknown means are collected. We develop a simple CUSUM-based methodology that provably control the probability of false alarms or the average run length while minimizing, in a minimax sense, the detection delay. We allow for all the model parameters to vary in order to capture a broad range of levels of statistical hardness for the problem at hand. We further show how our methodology is applicable to the case in which multiple change points are to be estimated sequentially.

An Efficient Algorithm and Quantization for Fully Distributed Sequential Change Detection

An Efficient Algorithm and Quantization for Fully Distributed Sequential Change Detection

Window-Limited CUSUM for Sequential Change Detection

Window-Limited CUSUM for Sequential Change Detection

Quickest detection of deception attacks on cyber–physical systems with a parsimonious watermarking policy

Adding a physical watermarking signal to the control input of a networked control system increases the detection probability of data deception attacks at the expense of increased control cost. This paper proposes a parsimonious policy to limit the average number of watermarking events when the attack is not present, which in turn reduces the control cost. We model the system as a stochastic optimal control problem and apply dynamic programming to minimize the average detection delay (ADD) for fixed upper bounds on false alarm rate (FAR) and an average number of watermarking events (ANW) before the attack. Under practical circumstances, the optimal solution results in a two threshold policy on the posterior probability of attack, derived from the Shiryaev statistics for sequential change detection and assuming the change point is a random variable. We derive asymptotically approximate analytical expressions of ADD and FAR, applying the non-linear renewal theory for non-independent and identically distributed data. The derived expressions reveal that ADD reduces with the increase in the Kullback–Leibler divergence (KLD) between the post- and pre-attack distributions of the test statistics. Therefore, we further design the optimal watermarking that maximizes the KLD for a fixed increase in the control cost. The relationship between the ANW and the increase in control cost is also derived. Simulation studies are performed to illustrate and validate the theoretical results.

Inference in High-Dimensional Online Changepoint Detection

We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for the changepoint, and second, we estimate the set of indices of coordinates in which the mean changes. We propose an online algorithm that produces an interval with guaranteed nominal coverage, and whose length is, with high probability, of the same order as the average detection delay, up to a logarithmic factor. The corresponding support estimate enjoys control of both false negatives and false positives. Simulations confirm the effectiveness of our methodology, and we also illustrate its applicability on the U.S. excess deaths data from 2017 to 2020. The supplementary material, which contains the proofs of our theoretical results, is available online.

Sequential Detection of an Arbitrary Transient Change Profile by the FMA Test

This article addresses the sequential detection of transient changes by using the finite moving average (FMA) test. It is assumed that a change occurs at an unknown (but nonrandom) change point and the duration of postchange period is finite and known. We relax the assumption that the profile of a transient change is chosen so that the log-likelihood ratios of the observations are associated random variables (r.v.s) in the prechange mode. Hence, the profile of the transient change is arbitrary and it is not necessarily of constant sign for a distribution with monotone likelihood ratio. A new upper bound for the worst-case probability of false alarm is proposed. It is shown that the optimization of the window-limited cumulative sum (CUSUM) test again leads to the FMA test. Three particular transient changes are considered: in the Gaussian mean, in the Gaussian variance, and in the parameter of exponential distribution. In the first case, a comparison between the bounds for the FMA test operating characteristics and the exact operating characteristics calculated by numerical integration is used to estimate the sharpness of the bounds. In the second and third cases, special attention is paid to the calculation of the FMA distribution in the case of arbitrary profile. The method of convolution is used to solve the problem.

E-detectors: A Nonparametric Framework for Sequential Change Detection

Sequential change detection is a classical problem with a variety of applications. However, the majority of prior work has been parametric, for example, focusing on exponential families. We develop a fundamentally new and general framework for sequential change detection when the pre- and post-change distributions are nonparametrically specified (and thus composite). Our procedures come with clean, nonasymptotic bounds on the average run length (frequency of false alarms). In certain nonparametric cases (like sub-Gaussian or sub-exponential), we also provide near-optimal bounds on the detection delay following a changepoint. The primary technical tool that we introduce is called an e-detector, which is composed of sums of e-processes—a fundamental generalization of nonnegative supermartingales—that are started at consecutive times. We first introduce simple Shiryaev-Roberts and CUSUM-style e-detectors, and then show how to design their mixtures in order to achieve both statistical and computational efficiency. Our e-detector framework can be instantiated to recover classical likelihood-based procedures for parametric problems, as well as yielding the first change detection method for many nonparametric problems. As a running example, we tackle the problem of detecting changes in the mean of a bounded random variable without i.i.d. assumptions, with an application to tracking the performance of a basketball team over multiple seasons.

A Dual-Channel Fluorescent Nanoprobe for Sequential Detection of ATP and Peroxynitrite to Accurately Distinguish between Normal Cells and Cancer Cells.

Cancer is one of the biggest public enemies of global health with its high morbidity and mortality. Achieving early diagnosis is the most effective means of reducing cancer harm, which requires the use of powerful tools to accurately identify biomarkers. However, most of the reported fluorescent probes for cancer diagnosis can only detect one substance, which makes it difficult to meet the requirements of high accuracy. Here, a fluorescent nanoprobe (CPQ@ZIF-90) for sequential detection of ATP and ONOO- is constructed by encapsulating the ONOO- sensitive unit CPQ within ZIF-90. CPQ@ZIF-90 first reacts with ATP to release CPQ, which greatly enhances the fluorescence at 740 nm. Then, the released CPQ continues to react with ONOO- and is oxidatively cleaved by ONOO- to form a coumarin product with a small π-conjugated structure, which significantly enhances the fluorescence at 510 nm. CPQ@ZIF-90 shows high sensitivity and selectivity for the detection of ATP and then ONOO-. Moreover, CPQ@ZIF-90 has good biocompatibility and successfully realizes the sequential detection of a dual-channel fluorescence change of ATP and ONOO- in living cells and zebrafish and accurately distinguishes normal cells from cancer cells. CPQ@ZIF-90 is expected to be a potential tool for accurate cancer diagnosis through sequential detection of two cancer markers.

Regressive Erosion at River Coca in Northeast Ecuador: Landslide Monitoring with Sentinel-1 to Support Disaster Risk Management

After the collapse of the San Rafael waterfall in Northeast Ecuador on 2 February 2020, a regressive erosion started along the River Coca putting national infrastructure, the environment and indigenous communities at risk. A fast monitoring of areas exposed to landslides on local scales therefore is necessary to provide adequate risk management for the region. The study area, located in the Andean tropics close to the volcano Reventador, is characterized by steep slopes, seismic activity and high rainfall throughout the year. Sentinel-1 SAR data provide a solution for time-series monitoring in the region as imagery is available day and night and not affected by cloud cover. Landslide monitoring with Sentinel-1 SAR data was implemented using a bi-temporal change detection (BCD) with SNAP and a sequential change detection (SCD) with EESA Docker and the Google Earth Engine (GEE) aiming at the identification of a suited approach for fast disaster monitoring and management. The SCD showed an overall accuracy of 0.91 compared to 0.88 using the BCD approach validated with high-resolution imagery. Based on the landslide detection, hazard variables could be further identified to support future hazard and risk assessment. Fast processing of Sentinel-1 time-series data in a cloud-based environment allows for near real-time monitoring of ongoing erosion and provides a potential for pro-active measures to protect the national economy, the environment and the society.

Adaptive Partially Observed Sequential Change Detection and Isolation

ABSTRACT High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev–Roberts procedure on the failure mode level and use the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.

Sequential detection of a temporary change in multivariate time series

In this work, we aim to provide a new and efficient recursive detection method for temporarily monitored signals. Motivated by the case of the propagation of an event over a field of sensors, we assumed that the change in the statistical properties in the monitored signals can only be temporary. Unfortunately, to our best knowledge, existing recursive and simple detection techniques such as the ones based on the cumulative sum (CUSUM) do not consider the temporary aspect of the change in a multivariate time series. In this paper, we propose a novel simple and efficient sequential detection algorithm, named Temporary-Event-CUSUM (TE-CUSUM). By combining with a new adaptive way to aggregate local CUSUM variables from each data stream, we empirically show that the TE-CUSUM has a very good detection rate in the case of an event passing through a field of sensors in a very noisy environment.

Sequential common change detection, isolation, and estimation in multiple poisson processes

In this article, motivated by detecting the occurrence of an epidemic when the arrival rates of patients increase in a portion of M panels or detecting the deterioration of a system composed of M independent components that causes an increase in failure rates in a portion of components, we consider the detection of a common change when M independent Poisson processes are monitored simultaneously where only a portion of the processes have rate increases after the change time. M individual cumulative sum (CUSUM) processes and Shiryaev-Roberts (S-R) processes are calculated recursively in parallel at each pooled arrival time. A systematic procedure is proposed by using the sum of M S-R processes as the detection process for a common change. After the detection, the M individual CUSUM processes are used to isolate the changed panels with false discovery rate (FDR) control and then the medians of the change time estimates from each individual CUSUM process or S-R process based on the isolated panels are used to estimate the common change time. The model can be generalized to different prechange rates, jittered change time, and unknown postchange rates.

Centralized and Decentralized Strategies for Sequential Detection of Transient Changes

Many practical problems of on-line fault detection in complex technical and cyber-physical systems can be reduced to the sequential reliable detection of transient changes. The goal of this paper is to continue the study of the transient change detection under less restrictive hypotheses than in previous publications. Specifically, it is assumed now that the fault dynamic profile can be arbitrary. Moreover, the new upper bounds for the probabilities of false alarm and missed detection are obtained under less restrictive hypotheses for the centralized and decentralized Finite Moving Average (FMA) test.

Combined Detection of Surface Changes and Deformation Anomalies Using Amplitude-Augmented Recursive InSAR Time Series

Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.

The air pollution monitoring by sequential detection of transient changes

According to the World Health Organization, the air pollution is a serious problem that threatens the human health and the environmental balance. The goal of this paper is to study the on-line detection of the air pollution augmentation by monitoring the level of the micro particles concentrations in the outdoor air. The proposed statistical approach is tested by using the real PM10 pollution data, which are provided by the air monitoring stations located in Strasbourg. First, an autoregressive model with exogenous variables is designed to describe the PM10 concentrations of several stations. Next, a special sequential change detection test is used to detect a transient change in the PM10 concentrations. Two versions of this test are proposed: the centralized Finite Moving Average (FMA) test and the decentralized FMA test. The criterion is based on the minimization of the missed detection probability provided that the false alarm rate is upper bounded. The theoretical analysis and the comparison of the proposed approaches on real data are discussed.

Optimal Parallel Sequential Change Detection Under Generalized Performance Measures

This paper considers the detection of change points in parallel data streams, a problem widely encountered when analyzing large-scale real-time streaming data. Each stream may have its own change point, at which its data has a distributional change. With sequentially observed data, a decision maker needs to declare whether changes have already occurred to the streams at each time point.Once a stream is declared to have changed, it is deactivated permanently so that its future data will no longer be collected. This is a compound decision problem in the sense that the decision maker may want to optimize certain compound performance metrics that concern all the streams as a whole. Thus, the decisions are not independent for different streams. Our contribution is three-fold. First, we propose a general framework for compound performance metrics that includes the ones considered in the existing works as special cases and introduces new ones that connect closely with the performance metrics for single-stream sequential change detection and large-scale hypothesis testing. Second, data-driven decision procedures are developed under this framework. Finally, optimality results are established for the proposed decision procedures. The proposed methods and theory are evaluated by simulation studies and a case study.