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In periods of dramatic stock price volatility, the identification of change points in stock price time series is important for analyzing the structural changes in financial market data, as well as for risk prevention and control in the financial market. As their residuals follow a generalized error distribution, the problem of estimating the change point parameters of the β -ARCH model is solved by combining the Kalman filtering method and the Bayes method innovatively, and we give a method for parameter estimation of the Bayes factors for the occurrences of change points, the expected values of the change point positions, and the variance of the change points. By detecting the change points of the price of eight stocks with a high number of limit up and limit down changes occurring in the observation period, the following conclusions are obtained: (1) Change point detection using the β -ARCH model based on the Bayes method is effective. (2) For different values of β , this research study finds that based on the classical ARCH model (i.e., β = 1 ) of the change point parameter, the results are relatively optimal. (3) The accuracy of change point detection can be improved by correcting stock short-term effects by using the Kalman filtering method. [ABSTRACT FROM AUTHOR]

This article focuses on detecting change points in high‐dimensional linear regression models with piecewise constant regression coefficients, moving beyond the conventional reliance on strict Gaussian or sub‐Gaussian noise assumptions. In the face of real‐world complexities, where noise often deviates into uncertain or heavy‐tailed distributions, we propose two tailored algorithms: a dynamic programming algorithm (DPA) for improved localization accuracy, and a binary segmentation algorithm (BSA) optimized for computational efficiency. These solutions are designed to be flexible, catering to increasing sample sizes and data dimensions, and offer a robust estimation of change points without requiring specific moments of the noise distribution. The efficacy of DPA and BSA is thoroughly evaluated through extensive simulation studies and application to real datasets, showing their competitive edge in adaptability and performance. [ABSTRACT FROM AUTHOR]

Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state of the monitored objects. Although there have been many surveys on time series segmentation, most of them focus more on change point detection (CPD) methods and overlook the advances in boundary detection (BD) and state detection (SD) methods. In this paper, we categorize time series segmentation methods into CPD, BD, and SD methods, with a specific focus on recent advances in BD and SD methods. Within the scope of BD and SD, we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category. As a conclusion, we found that: (1) Existing methods failed to provide sufficient support for online working, with only a few methods supporting online deployment; (2) Most existing methods require the specification of parameters, which hinders their ability to work adaptively; (3) Existing SD methods do not attach importance to accurate detection of boundary points in evaluation, which may lead to limitations in boundary point detection. We highlight the ability to working online and adaptively as important attributes of segmentation methods, the boundary detection accuracy as a neglected metrics for SD methods. [ABSTRACT FROM AUTHOR]

The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensions and context patterns. Third, decision-making should satisfy real-time requirements. To address the above challenges, we first construct a Non-Stationary Markov Decision Process (NSMDP) based on the assumption of ride-hailing service systems dynamics. Then, we develop a solution framework for the NSMDP. A change point detection method based on feature-enhanced LSTM within the framework can identify the changepoints and time-varying context patterns of stochastic demand. Moreover, the framework also includes a deterministic policy deep reinforcement learning algorithm to optimize. Finally, through simulated experiments with real-world historical data, we demonstrate the effectiveness of the proposed approach. It performs well in improving the platform's profits and alleviating supply-demand imbalances under the dynamic subsidy strategy. The results also prove that a scientific dynamic subsidy strategy is particularly effective in the high-demand context pattern with more drastic fluctuations. Additionally, the profitability of dynamic subsidy strategy will increase with the increase of the non-stationary level. [ABSTRACT FROM AUTHOR]

Change point detection is an important area of scientific research and has applications in a wide range of fields. In this paper, we propose a sequential change point detection (SCPD) procedure for mean-shift change point models. Unlike classical feature selection based approaches, the SCPD method detects change points in the order of the conditional change sizes and makes full use of the identified change points information. The extended Bayesian information criterion (EBIC) is employed as the stopping rule in the SCPD procedure. We investigate the theoretical property of the procedure and compare its performance with other methods existing in the literature. It is established that the SCPD procedure has the property of detection consistency. Simulation studies and real data analyses demonstrate that the SCPD procedure has the edge over the other methods in terms of detection accuracy and robustness. [ABSTRACT FROM AUTHOR]

This study delves into the complex relationship between teleconnection patterns and meteorological drought in Northwest China, highlighting the crucial influence of global circulation indices on regional drought dynamics. By analyzing precipitation and temperature data from 1962 to 2022 using the CN05.1 datasets and incorporating various global circulation indices, the study employs the Standardized Precipitation Evapotranspiration Index (SPEI) to delineate drought conditions. Utilizing the Modified Mann-Kendall test and segmented models for trend and change point detection, along with cross wavelet transforms (XWT) and wavelet coherence (WTC) analysis to examine the impact of 15 global circulation indices, the study uncovers significant spatial and temporal climatic variations. Findings indicate a significant increase in temperature and precipitation, with March-April-May (MAM) season showing pronounced drought severity mainly due to a significant temperature rise with a value of 0.0420 °C/year. Change point analysis reveals pivotal shifts in climate, highlighting the region’s susceptibility to climate change. The study identified strong correlations between drought occurrences and global circulation indices like the Artic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI), especially within a 4–8 year timeframe, pointing to the significant role of teleconnections in affecting local drought conditions. These insights are vital for formulating effective drought management and climate adaptation strategies in arid and semi-arid regions, offering valuable guidance for policymakers and researchers focused on improving water resource management and enhancing climate resilience in such vulnerable environments.

Multimode processes are common in modern industry and refer to processes that work in multiple operating modes. Motivated by the torque control process of a wind turbine, we determine that there exist two types of changes in multimode processes: (i) mode transitions and (ii) parameter changes. Detecting both types of changes is an important issue in practice, but existing methods mainly consider one type of change, and thus, do not work well. To address this issue, we propose a novel modeling framework for the offline change point detection problem of multimode processes, which simultaneously considers mode transitions and parameter changes. We characterize each mode with a parametric cost function and formulate the problem as an optimization model. In the model, two penalty terms penalize the number of change points, and a series of constraints specify the multimode characteristics. With certain assumptions, the asymptotic property ensures the accuracy of the model solution. To solve the model, we propose an iterative algorithm and develop a multimode-pruned exact linear time (multi-PELT) method for initialization. The simulation study and the real case study demonstrate the effectiveness of our method against the state-of-the-art methods in terms of the accuracy of change point detection, mode identification, and parameter estimation. [ABSTRACT FROM AUTHOR]

Abstract Expectations of big data across various fields, including education, are increasing. However, uncovering valuable insights from big data is like locating a needle in a haystack, and it is difficult for teachers to use educational big data on their own. This study aimed to understand changes in student participation rates during classes and teachers’ teaching styles by analyzing educational big data. In the analysis, data from 120 students and two mathematics class teachers at a public junior high school in Japan were used. We applied the pruned exact linear time (PELT) algorithm to automatically identify significant changes in student participation during class. Based on the information obtained, we analyzed the interaction logs of teachers’ e-book readers and clarified the relationship between student participation rates and teacher behavior patterns. Change point detection using the PELT algorithm showed a high F1-score of 0.7929, indicating good overall performance. We also investigated whether there was a relationship between class differences and teachers’ actions and found a statistically significant difference. The results provide clues for improving student learning engagement and teachers’ teaching styles, and they are expected to improve the quality of education by automatically identifying notable cases from educational big data. However, further research is required to improve data analysis methods, such as adjusting the parameters of algorithms based on the situation.

The task of modeling and analyzing intercepted multifunction radars (MFRs) pulse trains is vital for cognitive electronic reconnaissance. Existing methodologies predominantly rely on prior information or heavily constrained models, posing challenges for non-cooperative applications. This paper introduces a novel approach to model MFRs using a Bayesian network, where the conditional probability density function is approximated by an autoregressive kernel mixture network (ARKMN). Utilizing the estimated probability density function, a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains. Simulation results affirm the proposed method's efficacy in modeling MFRs, outperforming the state-of-the-art in pulse train denoising and change point detection.

With the new revolution in data technology, many types of streaming data are automatically generated in our living environment. The vast amount of information carried by this streaming data demands decision support from new online learning algorithms. In this paper, we propose a new online learning algorithm to monitor changes in stream data/system status and provide automatic decision support using streaming data. The new online learning algorithm we propose consists of two components. Firstly, we adapt an existing Bayesian approach into an online learning algorithm for change detection, which serves as Component A. Then, we integrate this algorithm with a modified online learning method from repeated games (Play against Random Past), which forms Component B. Theoretical support for Component B is provided with a mathematical proof at the end. We demonstrate the algorithm's performance in this paper through simulations using both artificial data from a random process and data from the 2009 H1N1 pandemic. [ABSTRACT FROM AUTHOR]

Gary Lorden provided several fundamental and novel insights into sequential hypothesis testing and change point detection. In this article, we provide an overview of Lorden's contributions in the context of existing results in those areas and some extensions made possible by Lorden's work. We also mention some of Lorden's significant consulting work, including as an expert witness and for NASA, the entertainment industry, and Major League Baseball. [ABSTRACT FROM AUTHOR]

Without imposing prior distributional knowledge underlying multivariate time series of interest, we propose a nonparametric change-point detection approach to estimate the number of change points and their locations along the temporal axis. We develop a structural subsampling procedure such that the observations are encoded into multiple sequences of Bernoulli variables. A maximum likelihood approach in conjunction with a newly developed searching algorithm is implemented to detect change points on each Bernoulli process separately. Then, aggregation statistics are proposed to collectively synthesize change-point results from all individual univariate time series into consistent and stable location estimations. We also study a weighting strategy to measure the degree of relevance for different subsampled groups. Simulation studies are conducted and shown that the proposed change-point methodology for multivariate time series has favorable performance comparing with currently available state-of-the-art nonparametric methods under various settings with different degrees of complexity. Real data analyses are finally performed on categorical, ordinal, and continuous time series taken from fields of genetics, climate, and finance. [ABSTRACT FROM AUTHOR]

Expectations of big data across various fields, including education, are increasing. However, uncovering valuable insights from big data is like locating a needle in a haystack, and it is difficult for teachers to use educational big data on their own. This study aimed to understand changes in student participation rates during classes and teachers' teaching styles by analyzing educational big data. In the analysis, data from 120 students and two mathematics class teachers at a public junior high school in Japan were used. We applied the pruned exact linear time (PELT) algorithm to automatically identify significant changes in student participation during class. Based on the information obtained, we analyzed the interaction logs of teachers' e-book readers and clarified the relationship between student participation rates and teacher behavior patterns. Change point detection using the PELT algorithm showed a high F1-score of 0.7929, indicating good overall performance. We also investigated whether there was a relationship between class differences and teachers' actions and found a statistically significant difference. The results provide clues for improving student learning engagement and teachers' teaching styles, and they are expected to improve the quality of education by automatically identifying notable cases from educational big data. However, further research is required to improve data analysis methods, such as adjusting the parameters of algorithms based on the situation. [ABSTRACT FROM AUTHOR]

Attack forecasting is a proactive approach to defend against cyber-attacks, as it helps in predicting future threats beforehand. In this paper, we propose a Distributed Denial of Service (DDoS) forecasting system, which is composed of two main components: the forecaster and the forecasting decision-making. The forecaster component uses a novel concept in cyber-attack forecasting, which combines time series forecasting analysis and multiple change points detection. The proposed concept improves forecasting accuracy compared to conventional statistical forecasting techniques, as it considers the abrupt and multiple changes in time series, where the multiple change points detection approach can help with auto-updating the forecasting models. Based on the proposed concept, we combine three time series analysis models and two change point detection algorithms to provide six forecasting method variants. Moreover, we propose an improved forecasting model named Ensemble Forecasting based on Time Series Analysis and Multiple Change Point Detection, which selects at each forecasting step the time series analysis model that exhibits the best accuracy and combines it with the change point detection algorithm. The proposed forecasting methods allow online learning as they can dynamically adapt to new observations. The forecasting decision-making component uses the output of the forecaster component, which is the number of forecasted attack flows and forecasting accuracy, and performs a risk assessment to alert the system administrator and take appropriate countermeasure decisions in advance. Experimentation results on CICDDoS2019 dataset show that the proposed methods significantly improve DDoS forecasting accuracy compared to traditional statistical forecasting models, such as Autoregressive, Exponential Smoothing, and Moving Average. [ABSTRACT FROM AUTHOR]

In multiple time series data, clustering the component profiles can identify meaningful latent groups while also detecting interesting change points in their trajectories. Conventional time series clustering methods, however, suffer the drawback of requiring the co-clustered units to have the same cluster membership throughout the entire time domain. In contrast to these "global" clustering methods, we develop a Bayesian "local" clustering method that allows the functions to flexibly change their cluster memberships over time. We design a Markov chain Monte Carlo algorithm to implement our method. We illustrate the method in several real-world datasets, where time-varying cluster memberships provide meaningful inferences about the underlying processes. These include a public health dataset to showcase the more detailed inference our method can provide over global clustering alternatives, and a temperature dataset to demonstrate our method's utility as a flexible change point detection method. for this article, including R codes implementing the method, are available online. [ABSTRACT FROM AUTHOR]

We look at a sequence of Bernoulli random variables where the success rates change from θ1 to θ2. We will assume that both the success rates before and after the change are unknown but increasing. We assume that this change does not happen abruptly but gradually over a period of time η where η is known. We calculate the probability that the change has started and completed. We also look at optimal stopping rules assuming that there is a cost for a false alarm and a cost per time unit to stop late. [ABSTRACT FROM AUTHOR]

This study aims to test for detecting a change point in the conditional quantile of general location-scale time series models. This issue is quite important in risk management because the conditional quantile is utilized to measure the value-at-risk or expected shortfall of financial assets. In this paper, we design two types of cumulative sum tests based on the conditional quantiles. Their limiting null distributions are derived under regularity conditions, together with consistency of the proposed tests under the alternative. Monte Carlo simulations demonstrate the good performance of the proposed tests in terms of both stability and power for various time series settings. A real data analysis using the daily returns of the Brent Oil futures also confirms the validity of the tests in real-world applications. [ABSTRACT FROM AUTHOR]

This study examines the changes in Dodoma between 2014 and 2021 using Landsat and night-time light (NTL) data. The results indicate that the built-up areas have grown by 47.1 km² between 2014 and 2021. The change point detection results of NTL time series data show that at one period (location 43) of the time series, the probability of a change is very high (> 0.70). This location corresponds to the year 2017. The Bayesian structural time series model results show that the relocation to Dodoma increased NTL by a statistically significant 81% (p < 0.001) from 2017. [ABSTRACT FROM AUTHOR]

This paper aims to develop Bayesian online change point detection (BOCD), a parametric change point detection method, into a nonparametric method to be able to detect change points in a free-distribution time series. Instead of using predefined exponential family distribution for predictive probability, we use kernel density estimation in which two possible options have been proposed. The first is manual constant bandwidth selection. This option provides a fast computation of KDE as it can follow dynamic programming. Another option for the best performance is a nonparametric bandwidth estimator. For the goal of fully nonparametric change point detection, the predefined hazard function in the BOCD method is changed to be a nonparametric estimator. The performance of the proposed method is intensively evaluated with simulated data and compared with other traditional methods. It is found that nonparametric BOCD gives a better solution in all cases as a consequence of the adaptive property of KDE. Furthermore, the real-life application of the method with real data demonstrated its outstanding performance in detecting change points across diverse datasets. This success signifies a promising solution for expanding the potential of change point detection algorithms across a broader range of fields through the use of a nonparametric approach. Nevertheless, it requires a sufficient amount of data to form a precise predictive distribution curve to accurately detect change points. [ABSTRACT FROM AUTHOR]

Drought, defined as the period when water availability considerably remains below normal levels for a certain time period, has devastating social, economic and environmental impacts. It is significant to closely monitor the temporal and spatial variability of drought in order to implement policies to mitigate the drought risk and ensure the sustainable management of water resources. In this study, three different drought indices, standard precipitation index (SPI), standardized precipitation‐evapotranspiration index (SPEI) and reconnaissance drought index (RDI), are calculated for 3‐, 6‐ and 12‐month time scales from the precipitation and temperature data measured in 11 meteorological observation stations within the Mediterranean coastal region of Türkiye during 1972–2020. Initially, two absolute homogeneity tests were applied, namely the standard normal homogeneity test (SNHT) and the Pettitt test, to detect abrupt changes in drought index time series. Then, trend analysis was carried out with the innovative trend analysis (ITA) and improved visualization of ITA (IV‐ITA) methods to determine the temporal variation of the drought in the region. A graphical representation is developed for the drought classifications based on IV‐ITA. As a result of the SNHT and Pettitt test, significant change points were found at a 5% significance level in 91.92% and 85.86% of all indices, respectively. In both tests, breaks were detected at similar times (between 1972 and 1974) at Marmaris station, while at Alanya station, changes were found between 1972 and 1973 for the SNHT and in 2008 for the Pettitt test. The trend differences between extreme conditions (extremely dry and wet) decreased with an increase in time scale, distinctively. The ITA trends were almost identical for 3‐month time scales, and they started to differ with time scale increases. Particularly, the presence of negative differences in data points contributes to the augmentation of drought periods, despite a few instances of extreme cases and minor fluctuations that can also be observed in IV‐ITA. The findings obtained from the study are expected to provide valuable information about the recent shifts in drought in the Mediterranean basin. [ABSTRACT FROM AUTHOR]

Understanding how temporal precipitation variability will behave in the future is crucial. Change point detection and sub-trend analysis were done on monthly total precipitation data acquired from the Coordinated Regional Climate Downscaling Experiment project in this study. Sub-periods have been determined based on the natural structure of the data set, as opposed to standard sub-period determination approaches. For the period 1971–2100, six different time series were used under three global climate models (CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, MPI-M-MPI-ESM-LR) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5). The study area has been selected to be the Konya Endorheic Basin in Turkey, which has been significantly influenced by climate change and human impacts. For change point detection, the Pruned Exact Linear Time method (PELT) and Binary Segmentation (BS) were employed, for trend analysis, Mann Kendall (MK) and Şen-Innovative Trend Analysis (Şen-ITA), and for both change point detection and sub-trend analysis, the Onyutha Trend Test (OTT) was utilized. The findings revealed four change points and five sub-periods. Summer precipitation has reduced considerably at a 95% significance level. There is strong evidence that precipitation has decreased between 1981 and 2070. Precipitation decreases similarly in the middle basin for the 1981–2000 and 2041–2070 years. [ABSTRACT FROM AUTHOR]

Online monitoring of social networks offers exciting features for platforms, enabling both technical and behavioral analysis. Numerous studies have explored the adaptation of traditional quality control methods for detecting change points within social networks. However, the current research studies face limitations such as an overreliance on case-based attributes, high computational costs, poor scalability with large networks, and low sensitivity in fast change point detection. This paper proposes a novel algorithm for social network monitoring using One-Class Support Vector Machines (OC-SVMs) to address these limitations. Additionally, using both nodal and network-level attributes makes it versatile for diverse social network applications and effectively detecting network disturbances. The algorithm utilizes a well-defined training data dictionary with an updating procedure for evolutionary networks, enhancing memory and time efficiency by reducing the processing of input data. Extensive numerical experiments are conducted using an EpiCNet model to simulate interactions in an online social network, covering six change scenarios to evaluate the proposed methodology. The results show lower Average Run Length (ARL) and Expected Delay Detection (EDD), demonstrating the superior accuracy and effectiveness of the OC-SVM algorithm compared to alternative methods. Applying OC-SVM to the Enron Email network indicates its capability to identify change points, reflecting the tumultuous timeline that led to Enron's downfall. This further validates the substantial advancement of OC-SVM in social network monitoring and opens doors to broader real-world applications. [ABSTRACT FROM AUTHOR]

Road traffic mortalities (RTMs) and injuries are among the leading causes of human fatalities worldwide, particularly in low-and middle-income countries like Iran. Using an interrupted time series analysis, we investigated three interventional points (two government-mandated fuel price increases and increased traffic ticket fines) for their potential relation to RTMs. Our findings showed that while the overall trend of RTMs was decreasing during the study period, multiple individual provinces showed smaller reductions in RTMs. We also found that both waves of government-mandated fuel price increases coincided with decreases in RTMs. However, the second wave coincided with RTM decreases in a smaller number of provinces than the first wave suggesting that the same type of intervention may not be as effective when repeated. Also, increased traffic ticket fines were only effective in a small number of provinces. Potential reasons and solutions for the findings are discussed in light of Iran's Road Safety Strategic Plan. [ABSTRACT FROM AUTHOR]

We propose a new model averaging approach to investigate segment regression models with multiple threshold variables and multiple structural breaks. We first fit a series of models, each with a single threshold variable and multiple breaks over its domain, using a two‐stage change point detection method. Then these models are combined together to produce a weighted ensemble through a frequentist model averaging approach. Consequently, our segment regression model averaging (SRMA) method may help identify complicated subgroups in a heterogeneous study population. A crucial step is to determine the optimal weights in the model averaging, and we follow the familiar non‐concave penalty estimation approach. We provide theoretical support for SRMA by establishing the consistency of individual fitted models and estimated weights. Numerical studies are carried out to assess the performance in low‐ and high‐dimensional settings, and comparisons are made between our proposed method and a wide range of existing alternative subgroup estimation methods. Two real economic data examples are analyzed to illustrate our methodology. [ABSTRACT FROM AUTHOR]

The goal of change point detection (CPD) is to identify abrupt changes in the statistics of signals or time series that reflect transitions in the underlying system's properties or states. While many statistical and learning-based approaches have been proposed to address this task, most state-of-the-art methods still treat this problem in an unsupervised setting. As a result, there is often a large gap between the algorithm-detected results and the expected outcomes of the user. To bridge this gap, we propose an active-learning strategy for the CPD problem that combines with the one-class support vector machine (OCSVM) model, resulting in an interactive CPD algorithm that improves itself by querying the end-user. This approach enables us to focus on detecting the desired change points and ignore false-positives or irrelevant change points. We demonstrate that the interactive OCSVM model can be combined with various unsupervised CPD models to function in a semi-supervised setting, resulting in improved detection accuracy. Our experimental results on various simulated and real-life datasets demonstrate a significant improvement in detection performance on both single- and multi-channel time series, even with a limited number of queries. [ABSTRACT FROM AUTHOR]

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes. [ABSTRACT FROM AUTHOR]

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model's deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called "embedding") generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats. [ABSTRACT FROM AUTHOR]

The application of data analysis tools and procedures to perceive value from vast volume of data created by connected IoT devices is known as IoT data analytics. While predictive analytics on IoT dealing with the prediction involved with the setting of IoT appliances, Prescriptive analytics is the next stage of IoT data analytics involves deriving actionable insights from predictions made in previous stages. The incorporation of time-dependent parameters in prescriptive models provides a more accurate depiction of a complex environment and the decision-making process that goes along with it. The scope of our work is to recommend prescriptive analytical models that make better decisions through the analysis of dynamic IoT data stream in real-time and prescribe an optimal solution. We carry out an analysis of time-series data to identify the patterns of data and learn how they change. In this direction, we attempt to represent time-series data by reducing its length, forecast change points, map change points to prescribed actions, and propose optimal decisions ahead of time events. In this paper, an overview of IoT data analytics, survey of prescriptive analytical models, applications, issues, challenges and platforms for IoT analytics are discussed. [ABSTRACT FROM AUTHOR]

Nowadays, Anomaly Detection (AD) models are incorporated into various systems, but they will become useless if they are not updated (i.e., re-trained) to keep up with changes in their external environment. When trying to automatically trigger AD model updates in response to environmental changes, one promising solution is considered to be the use of a Change Point Detection (CPD) method. Most existing methods impose stationary or Independent and Identically Distributed (IID) constraints on the target time-series, and therefore are not suitable for our target time-series in telecommunications that often fluctuate periodically. In this paper, we propose a new clustering-based CPD method for detecting changes in non-stationary time-series. The proposed method enables pattern changes to be detected by tracking cluster transitions and calculating the distance between the cluster transition distributions for the past and current periods. The accuracy of the proposed CPD method itself and the effect of re-training the AD model at the detected change points are shown by using real hourly time-series data at mobile base stations in the Tokyo Metropolitan Area for nearly one year.

This article studies the use of airborne global navigation satellite system reflectometry techniques for remote sensing applications at regional scale. The objective is to classify the reflectivity of airborne global navigation satellite system (GNSS) signals in order to differentiate various reflective surfaces along the satellite traces. For this purpose, we propose a segmentation algorithm based on an online change point detector and an offline change point localization estimate. Given the presence of speckle noise in GNSS signals, a homomorphic log-transformation is applied to mitigate this noise. In this context, the cumulative sum change point detector and the maximum likelihood change point localization are designed for a log-gamma distribution. We show that the proposed radar segmentation system is able to automatically detect different landforms along real flight experiments that took place in the northern region of France. Automatic classification using K-means clustering is applied to the segmented signals allowing to distinguish different segments of the signal.

This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially $\alpha$, $\beta$, and fast $\phi$-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length ($ {\mathtt {ARL}}$) and upper bounds for average-detection-delay ($ {\mathtt {ADD}}$) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast $\phi$-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially $\alpha$/$\beta$-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.

This paper focuses on the long-overlooked phenomenon that individual travel patterns are not always stable over the long term and may change due to seasonal changes, moving, and work schedule changes. Unlike previous studies that identified sudden peak points, this paper treats travel pattern change as a change point detection problem in a time series and defines change as “sudden, substantial, and continuous”. Considering the complexity of travel behavior, this paper measures changes in travel patterns in three dimensions: time, space, and frequency, and establishes a Bayesian change point detection model. A nine-month period of private car GPS data from Aichi, Japan, is used for an example analysis. The results show that the Bayesian approach can effectively identify travel pattern changes. Compared with the traditional GLR, the proposed method in this paper has higher recognition accuracy with lower model complexity. Meanwhile, the experimental results show that individual travel patterns may change in only one dimension or in multiple dimensions at the same time. Based on this, the correlation analysis of travel patterns in the temporal and spatial dimensions is carried out, and it is verified that there is a certain positive correlation between the two. The Bayesian change-point detection model proposed is robust and generally applicable to other fields besides travel patterns.

In periods of dramatic stock price volatility, the identification of change points in stock price time series is important for analyzing the structural changes in financial market data, as well as for risk prevention and control in the financial market. As their residuals follow a generalized error distribution, the problem of estimating the change point parameters of the β-ARCH model is solved by combining the Kalman filtering method and the Bayes method innovatively, and we give a method for parameter estimation of the Bayes factors for the occurrences of change points, the expected values of the change point positions, and the variance of the change points. By detecting the change points of the price of eight stocks with a high number of limit up and limit down changes occurring in the observation period, the following conclusions are obtained: (1) Change point detection using the β-ARCH model based on the Bayes method is effective. (2) For different values of β, this research study finds that based on the classical ARCH model (i.e., β=1) of the change point parameter, the results are relatively optimal. (3) The accuracy of change point detection can be improved by correcting stock short-term effects by using the Kalman filtering method.

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

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. [ABSTRACT FROM AUTHOR]

To mitigate the current COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt, accurate and actionable estimations of lockdown and social distancing policy adherence. Transport for London, the local transportation department, reports they implemented over 700 interventions such as greater signage and expansion of pedestrian zoning at the height of the pandemic's first wave with our platform providing key data for those decisions. Large well-defined heterogeneous compositions of pedestrian footfall and physical proximity are difficult to acquire, yet necessary to monitor city-wide activity (busyness) and consequently discern actionable policy decisions. To meet this challenge, we leverage our existing large-scale data processing urban air quality machine learning infrastructure to process over 900 camera feeds in near real-time to generate new estimates of social distancing adherence, group detection and camera stability. In this work, we describe our development and deployment of a computer vision and machine learning pipeline. It provides near immediate sampling and contextualization of activity and physical distancing on the streets of London via live traffic camera feeds. We introduce a platform for inspecting, calibrating and improving upon existing methods, describe the active deployment on real-time feeds and provide analysis over an 18 month period. [ABSTRACT FROM AUTHOR]

Software reliability growth models (SRGMs) are essential for forecasting and controlling the reliability of software systems. In present article, we propose an enhanced SRGM that incorporates three important factors: testing coverage, testing effort, and change point detection. We introduce a novel testing coverage function that captures the delayed S-shaped behaviour commonly observed in software reliability growth. Weibull distribution is utilized to model the testing effort. Finally, we address the impact of change points in software reliability. To assess how well our suggested model works, we conducted experiments using real-world software failure data provide by Tandem computers. The results demonstrate that our model outperforms existing SRGMs by providing more accurate predictions and a better understanding of the interplay between testing coverage, testing effort, and change points. [ABSTRACT FROM AUTHOR]

The lack of publicly open network traffic datasets for research purposes is hindering machine learning applications to wireless network analysis and design. In this work, a number of published traffic throughput temporal evolutions are digitized and used for traffic anomaly and change point detection. The mean traffic temporal evolutions are extracted by digitizing graphical curves and organized into a time series dataset. The procedure of reusing such dataset for testing traffic analysis models is presented. The procedure consists of the following steps. Firstly, α -stable distributed traffic variations are added to the mean throughput values. Secondly, traffic anomalies are generated by analyzing traffic redistribution after switching on and off base stations and sectors. Lastly, traffic trend changes are introduced to model impact of global events such as COVID-19 pandemic. Several machine learning models are used to illustrate applicability of created dataset for detecting traffic anomalies and trend changes. [ABSTRACT FROM AUTHOR]

Identifying the existence and locations of change points has been a broadly encountered task in many statistical application areas. The existing change point detection methods may produce unsatisfactory results for high‐dimensional data since certain distributional assumptions are made on data, which are hard to verify in practice. Moreover, some parameters (such as the number of change points) need to be estimated beforehand for some methods, making their powers sensitive to these values. Here, we propose a kernel‐based U$$ U $$‐statistic to identify change points (KUCP) for high dimensional data, which is free of distributional assumptions and sup‐parameter estimations. Specifically, we employ a kernel function to describe similarities among the subjects and construct a U$$ U $$‐statistic to test the existence of change point for a given location. The asymptotic properties of the U$$ U $$‐statistic are deduced. We also develop a procedure to locate the change points sequentially via a dichotomy algorithm. Extensive simulations demonstrate that KUCP has higher sensitivity in identifying existence of change points and higher accuracy in locating these change points than its counterparts. We further illustrate its practical utility by analyzing a gene expression data of human brain to detect the time point when gene expression profiles begin to change, which has been reported to be closely related with aging brain. [ABSTRACT FROM AUTHOR]

The task of detecting a structural change in conditional quantiles of time series with time-varying volatilities is vital in the field of financial time series, especially risk management. Therefore, in this study, we aim to construct a change point test to detect a quantile change by hybridizing the cumulative sum of squares test with the support vector quantile regression. Compared to the test employing the standard quantile regression, this approach not only provides more robust property against a high degree of nonlinearity of time series but also exhibits better performance for the datasets contaminated with high-frequency noises, as demonstrated in our simulation study. It also has merits to provide more diverse interpretations and a deeper understanding of financial time series than the volatility change point test, as illustrated in a real data analysis of three financial indices, namely, S&P500, Nasdaq composite index, and the stock price of Apple Inc. [ABSTRACT FROM AUTHOR]

This study demonstrates whether financial text is useful for the tactical asset allocation method using stocks. This can be achieved using natural language processing to create polarity indexes in financial news. We perform clustering of the created polarity indexes using the change point detection algorithm. In addition, we construct a stock portfolio and rebalanced it at each change point using an optimization algorithm. Consequently, the proposed asset allocation method outperforms the comparative approach. This result suggests that the polarity index is useful for constructing the equity asset allocation method. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient statistical method (denoted as 'Adaptive Resources Allocation CUSUM') to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples. [ABSTRACT FROM AUTHOR]

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional–convolutional neural network–long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics. [ABSTRACT FROM AUTHOR]