Change Point Detection Algorithm Research Articles

Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness.

Real-Time Detection of State Transitions in Stochastic Signals from Biological Systems.

Robust analysis of signals from stochastic biomolecular processes is critical for understanding the dynamics of biological systems. Measured signals typically show multiple states with heterogeneities and a wide range of state lifetimes. Here, we present an algorithm for robust detection of state transitions in experimental time traces where the properties of the underlying states are a priori unknown. The method implements a maximum-likelihood approach to fit models in neighboring windows of data points. Multiple windows are combined to achieve a high sensitivity for state transitions with a wide range of lifetimes. The proposed maximum-likelihood multiple-windows change point detection (MM-CPD) algorithm is computationally extremely efficient and enables real-time signal analysis. By analyzing both simulated and experimental data, we demonstrate that the algorithm provides accurate change point detection in time traces with multiple heterogeneous states that are a priori unknown. A high sensitivity for a wide range of state lifetimes is achieved.

Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

SummaryState-level policy interventions have been critical in managing the spread of the new coronavirus. Here, we study the lag time between policy interventions and change in COVID-19 outcome trajectory in the United States. We develop a stepwise drifts random walk model to account for non-stationarity and strong temporal correlation and subsequently apply a change-point detection algorithm to estimate the number and times of change points in the COVID-19 outcome data. Furthermore, we harmonize data on the estimated change points with non-pharmaceutical interventions adopted by each state of the United States, which provides us insights regarding the lag time between the enactment of a policy and its effect on COVID-19 outcomes. We present the estimated change points for each state and the District of Columbia and find five different emerging trajectory patterns. We also provide insight into the lag time between the enactment of a policy and its effect on COVID-19 outcomes.

Change Point Enhanced Anomaly Detection for IoT Time Series Data

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

Change point detection for compositional multivariate data

Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.

Detecting change points in dynamic networks by measuring cluster stability

Clustering patterns are ubiquitously present in a variety of networked systems, and may change with the evolution of network topology. Probing into the cluster structures can shed light on the change of the entire network, especially those sudden changes emerging in the process of network evolution. Though abundant researches have been done in detecting the changes of dynamic networks, more precisely, change points at which the network topology experiences abrupt changes, most of the existing methods focus on local changes (e.g. edges change) that are commonly mixed with noise, giving rise to high false positive reports. Different from the previous work, here we inspect the topological changes from mesoscale clusters of dynamic networks, which will reduce the perturbation of link variation to detection accuracy. Towards this end, we look for the invariant clusters of nodes during the observation window in dynamic networks and propose a new measure to quantify the stability of node clusters with respect to the invariant clustering patterns. Then the change of dynamic networks at mesoscale can be captured by comparing the variations of stability measures. In the light of the proposed measurement, we design a change-point detection algorithm and conduct extensive experiments on synthetic and real-life datasets to demonstrate the effectiveness of our method. The results show the outperformance of our method in identifying change points, compared to several baseline methods.

Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences

Motivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions. In particular, BHM yields the smallest detection error compared with other competitive methods considered in the paper. We utilize a local scan procedure to accelerate the computation, while the anomaly locations are determined by maximizing the posterior probability through dynamic programming. We establish consistency of the estimated number and locations of the change points and conduct extensive simulations to evaluate the BHM approach. Among the popular change point detection algorithms, BHM yields the best performance for most of the datasets in terms of the F1 score for the wind turbine anomaly detection. Supplementary materials for this article are available online.

Unsupervised Offline Changepoint Detection Ensembles

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.

Product technical life prediction based on multi-modes and fractional Lévy stable motion

Some equipment degradation processes have long-range dependence (LRD) and multi-modes characteristics. The multi-modes are caused by changes of the external environment, the operating conditions and the loads throughout the lifetime of the equipment. In the present paper, a multi-modal Fractional Lévy Stable Motion (FLSM) degradation model is developed to predict the product technical life or remaining useful life (RUL) of equipment. The advantage of FLSM lies in its LRD characteristics and its ability to describe multiple stochastic distributions as the tail parameter α changes. Multi-modes, switching points and modal categories are identified by change point detection and clustering algorithms, and a Markov state transition matrix describes the modes switching law. The probability density function (PDF) of RUL is established by Monte Carlo Simulation. The effectiveness of the prediction model is verified by a practical example of a blast furnace.

Automatic identification of rock formation interface based on borehole imaging

ABSTRACT Borehole imaging, one of the main methods of rock formation detection, is widely used in mining, tunnels, petroleum, and other fields of engineering. However, the method of manually identifying the characteristic of rock formation based on borehole imaging is greatly affected by subjective factors. For example, it should be noted that when the rock layer color is similar, the position of the rock interface may be different. In this paper, a method of quantitatively describing the characteristics of the rock formation is proposed, and the automatic recognition of the rock interface is realized. Firstly, the calculation method of rock dip angle and position based on borehole image with 360° wall surface development is proposed. Secondly, the three-dimensional distribution characteristics of the gray values of the rock layer are used to quantitatively describe the characteristics of the rock layer. Finally, an automatic recognition method of rock formation interface based on change-point detection algorithm is proposed. In addition, the recognition effect of the rock formation interface is analyzed by experiments and applied in the field. Research shows that, compared with method two, method one can determine the rock inclination. But its anti-interference ability is poor. Method two can better determine the position of the rock interface. The method is of great significance to the intelligent development of the borehole imaging system and promotes safe, efficient, and sustainable mining in coal mines.

Improved PV Soiling Extraction Through the Detection of Cleanings and Change Points

Photovoltaic (PV) soiling profiles exhibit a sawtooth shape, where cleaning events and soiling deposition periods alternate. Generally, the rate at which soiling accumulates is assumed to be constant within each deposition period. In reality, changes in rates can occur because of sudden variations in climatic conditions, e.g., dust storms or prolonged periods of rain. The existing models used to extract the soiling profile from the PV performance data might fail to capture the change points and occasionally estimate incorrect soiling profiles. This work analyzes how the introduction of change points can be beneficial for soiling extraction. Data from nine soiling stations and a 1-MW site were analyzed by using piecewise regression and three change point detection algorithms. The results showed that accounting for change points can provide significant benefits to the modeling of soiling even if not all the change point algorithms return the same improvements. Considering change points in historical trends is found to be particularly important for studies aiming to optimize cleaning schedules.

Exposing Speech Transsplicing Forgery with Noise Level Inconsistency

Splicing is one of the most common tampering techniques for speech forgery in many forensic scenarios. Some successful approaches have been presented for detecting speech splicing when the splicing segments have different signal-to-noise ratios (SNRs). However, when the SNRs between the spliced segments are close or even same, no effective detection methods have been reported yet. In this study, noise inconsistency between the original speech and the inserted segment from other speech is utilized to detect the splicing trace. First, noise signal of the suspected speech is extracted by a parameter-optimized noise estimation algorithm. Second, the statistical Mel frequency features are extracted from the estimated noise signal. Finally, the spliced region is located by utilizing a change point detection algorithm on the estimated noise signal. The effectiveness of the proposed method is evaluated on a well-designed speech splicing dataset. The comparative experimental results show that the proposed algorithm can achieve better detection performance than other algorithms.

Post-selection inference for changepoint detection algorithms with application to copy number variation data.

Changepoint detection methods are used in many areas of science and engineering, for example, in the analysis of copy number variation data to detect abnormalities in copy numbers along the genome. Despite the broad array of available tools, methodology for quantifying our uncertainty in the strength (or the presence) of given changepoints post-selection are lacking. Post-selection inference offers a framework to fill this gap, but the most straightforward application of these methods results in low-powered hypothesis tests and leaves open several important questions about practical usability. In this work, we carefully tailor post-selection inference methods toward changepoint detection, focusing on copy number variation data. To accomplish this, we study commonly used changepoint algorithms: binary segmentation, as well as two of its most popular variants, wild and circular, and the fused lasso. We implement some of the latest developments in post-selection inference theory, mainly auxiliary randomization. This improves the power, which requires implementations of Markov chain Monte Carlo algorithms (importance sampling and hit-and-run sampling) to carry out our tests. We also provide recommendations for improving practical useability, detailed simulations, and example analyses on array comparative genomic hybridization as well as sequencing data.

Facilitating Human Activity Data Annotation via Context-Aware Change Detection on Smartwatches

Annotating activities of daily living (ADL) is vital for developing machine learning models for activity recognition. In addition, it is critical for self-reporting purposes such as in assisted living where the users are asked to log their ADLs. However, data annotation becomes extremely challenging in real-world data collection scenarios, where the users have to provide annotations and labels on their own. Methods such as self-reports that rely on users’ memory and compliance are prone to human errors and become burdensome since they increase users’ cognitive load. In this article, we propose a light yet effective context-aware change point detection algorithm that is implemented and run on a smartwatch for facilitating data annotation for high-level ADLs. The proposed system detects the moments of transition from one to another activity and prompts the users to annotate their data. We leverage freely available Bluetooth low energy (BLE) information broadcasted by various devices to detect changes in environmental context. This contextual information is combined with a motion-based change point detection algorithm, which utilizes data from wearable motion sensors, to reduce the false positives and enhance the system's accuracy. Through real-world experiments, we show that the proposed system improves the quality and quantity of labels collected from users by reducing human errors while eliminating users’ cognitive load and facilitating the data annotation process.

Change-point detection: application of Cusum method to real life data

Several procedures have been developed for the detection of abrupt changes in time series. Among these procedures, it can be mentioned the Cumulative Sum (Cusum) type method. It is in such a perspective that Katchekpele et al. (2017) proposed a method using a Cusum type test to detect a change-point in the unconditional variance of the generalised autoregressive conditional heteroskedasticity(GARCH) models. The aim of this paper is to present an application of their technique. After briefly recalling how the test statistic was constructed, the change-point detection algorithm is given and it is shown how it is applied to some real life data.

Optimal nonparametric change point analysis

We study change point detection and localization for univariate data in fully nonparametric settings, in which at each time point, we acquire an independent and identically distributed sample from an unknown distribution that is piecewise constant. The magnitude of the distributional changes at the change points is quantified using the Kolmogorov–Smirnov distance. Our framework allows all the relevant parameters, namely the minimal spacing between two consecutive change points, the minimal magnitude of the changes in the Kolmogorov–Smirnov distance, and the number of sample points collected at each time point, to change with the length of the time series. We propose a novel change point detection algorithm based on the Kolmogorov–Smirnov statistic and show that it is nearly minimax rate optimal. Our theory demonstrates a phase transition in the space of model parameters. The phase transition separates parameter combinations for which consistent localization is possible from the ones for which this task is statistically infeasible. We provide extensive numerical experiments to support our theory.

Online detection of failures generated by storage simulator

Modern large-scale data-farms consist of hundreds of thousands of storage devices that span distributed infrastructure. Devices used in modern data centers (such as controllers, links, SSD- and HDD-disks) can fail due to hardware as well as software problems. Such failures or anomalies can be detected by monitoring the activity of components using machine learning techniques. In order to use these techniques, researchers need plenty of historical data of devices in normal and failure mode for training algorithms. In this work, we challenge two problems: 1) lack of storage data in the methods above by creating a simulator and 2) applying existing online algorithms that can faster detect a failure occurred in one of the components.We created a Go-based (golang) package for simulating the behavior of modern storage infrastructure. The software is based on the discrete-event modeling paradigm and captures the structure and dynamics of high-level storage system building blocks. The package's exible structure allows us to create a model of a real-world storage system with a configurable number of components. The primary area of interest is exploring the storage machine's behavior under stress testing or exploitation in the medium-or long-term for observing failures of its components.To discover failures in the time series distribution generated by the simulator, we modified a change point detection algorithm that works in online mode. The goal of the change-point detection is to discover differences in time series distribution. This work describes an approach for failure detection in time series data based on direct density ratio estimation via binary classifiers.

An entropy-based approach to the study of human mobility and behavior in private homes.

Understanding human mobility in outdoor environments is critical for many applications including traffic modeling, urban planning, and epidemic modeling. Using data collected from mobile devices, researchers have studied human mobility in outdoor environments and found that human mobility is highly regular and predictable. In this study, we focus on human mobility in private homes. Understanding this type of human mobility is essential as smart-homes and their assistive applications become ubiquitous. We model the movement of a resident using ambient motion sensor data and construct a chronological symbol sequence that represents the resident’s movement trajectory. Entropy rate is used to quantify the regularity of the resident’s mobility patterns, and an upper bound of predictability is estimated. However, the presence of visitors and malfunctioning sensors result in data that is not representative of the resident’s mobility patterns. We apply a change-point detection algorithm based on penalized contrast function to detect these changes, and to identify the time periods when the data do not completely reflect the resident’s activities. Experimental results using the data collected from 10 private homes over periods of 178 to 713 days show that human mobility at home is also highly predictable in the range of 70% independent of variations in floor plans and individual daily routines.

Bayesian Online Change Point Detection for Baseline Shifts

In time series data analysis, detecting change points on a real-time basis (online) is of great interest in many areas, such as finance, environmental monitoring, and medicine. One promising means to achieve this is the Bayesian online change point detection (BOCPD) algorithm, which has been successfully adopted in particular cases in which the time series of interest has a fixed baseline. However, we have found that the algorithm struggles when the baseline irreversibly shifts from its initial state. This is because with the original BOCPD algorithm, the sensitivity with which a change point can be detected is degraded if the data points are fluctuating at locations relatively far from the original baseline. In this paper, we not only extend the original BOCPD algorithm to be applicable to a time series whose baseline is constantly shifting toward unknown values but also visualize why the proposed extension works. To demonstrate the efficacy of the proposed algorithm compared to the original one, we examine these algorithms on two real-world data sets and six synthetic data sets.

Construction of a Power Quality Monitoring System Based on the Wavelet Transform Change-Point Detection Algorithm on the Virtual Instrument Platform

It is aimed to carry out the investigation on power quality detection, promote the realization of efficient transmission of network data, and expand the application of wavelet transform change-point detection algorithm in the monitoring system. The voltage deviation is used as a starting point to explore the detection of power quality. First, it describes the harmonics of the public power grid and the limits of harmonic voltage. Second, based on the virtual instrument platform, the power quality monitoring system based on wavelet transform change-point detection algorithm is completed. Finally, by adding a monitoring terminal and a service terminal, the design of the monitoring system server is completed. Through the analysis of the experimental results, it is found that in the monitoring system, the current waveform and the three-phase voltage can be accurately displayed. The combined design of the networked monitoring system and the system server enables the system to complete the rapid transmission of data related to power quality, while having a good monitoring effect. For the optimization of networked monitoring experienceof the server, the application of wavelet transform in power quality measurement is realized. The power quality monitoring system proposed has a strong practicality in power quality monitoring.