Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time s...Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.展开更多
The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detectio...The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.展开更多
A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contaminat...A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contamination" caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.展开更多
Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whe...Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whereas trend can be defined as estimation of gradual departure from past norms.We analyze the time series data in the presence of trend,using Cox-Stuart methods together with the change point algorithms.We applied the methods to the nearsurface wind speed time series for Australia as an example.The trends in near-surface wind speeds for Australia have been investigated based upon our newly developed wind speed datasets,which were constructed by blending observational data collected at various heights using local surface roughness information.The trend in wind speed at 10 m is generally increasing while at 2 m it tends to be decreasing.Significance testing,change point analysis and manual inspection of records indicate several factors may be contributing to the discrepancy,such as systematic biases accompanying instrument changes,random data errors(e.g.accumulation day error)and data sampling issues.Homogenization technique and multiple-period trend analysis based upon change point detections have thus been employed to clarify the source of the inconsistencies in wind speed trends.展开更多
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 constrai...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.展开更多
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 t...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.展开更多
Taking Shanghai as an example,this study obtained the online travel notes data from Xiaohongshu and Qunar in the past 10 years to construct the Shanghai tourist flow network(STFN)and used the methods of change point d...Taking Shanghai as an example,this study obtained the online travel notes data from Xiaohongshu and Qunar in the past 10 years to construct the Shanghai tourist flow network(STFN)and used the methods of change point detection(CPD)and complex network analysis(CNA)to reveal the spatial structure characteristics of Shanghai tourism flow and the dynamic evolution process of STFN.The results showed that:(1)In the past 10 years,Shanghai tourist market had experienced a process of evolution from stable and orderly to short-term fluc-tuation and then gradual recovery,and the year of 2019 was the turning point of tourist flow network evolution.(2)The small-world and approximate scale-free characteristics of STFN were verified,and the network changed from disassortative to temporary assortative,showing a development trend of external expansion and internal separation.(3)While the centrality indicators of tourist flow network remained stable as a whole,the attention to cultural nodes was also increasing with the emergence of new nodes;(4)In terms of spatial connection,new popular nodes emerged and the relationship between them and the surrounding nodes was strengthened;(5)The spatial pattern of tourist flow network presented an inverted“V”shape and gradually expanded to southwest and southeast,forming a network with core nodes as the center and radiating outward.At the same time,newly emerging nodes at the periphery had formed relatively independent clusters.展开更多
In this paper,the authors consider the problem of change points within the framework of model selection and propose a procedure for estimating the locations of change points when the number of change points is known.T...In this paper,the authors consider the problem of change points within the framework of model selection and propose a procedure for estimating the locations of change points when the number of change points is known.The strong consistency of this procedure is also established. The problem of detecting change points is discussed within the framework of the simultaneous test procedure.The case where the number of change points is unknown will be discussed in another paper.展开更多
The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution gr...The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.展开更多
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 dynami...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℃/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.展开更多
基金support by the Federal Ministry for Economic Affairs and Climate Action of Germany(BMWK)within the Innovation Platform“KEEN-Artificial Intelligence Incubator Laboratory in the Process Industry”(Grant No.01MK20014T)The research of L.B.is supported by the Swedish Research Council Grant VR 2018-03661。
文摘Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.
基金Project(2011AA040603) supported by the National High Technology Ressarch & Development Program of ChinaProject(201202226) supported by the Natural Science Foundation of Liaoning Province, China
文摘The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.
基金Projects(90820302,60805027)supported by the National Natural Science Foundation of ChinaProject(2011BAK15B06)supported by the National Science and Technology Support Program,China+1 种基金Project(2013M541003)supported by the China Postdoctoral Science FoundationProject(2012YQ090208)supported by the Special-Funded Program on National Key Scientific Instruments and Equipment Development
文摘A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contamination" caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.
文摘Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whereas trend can be defined as estimation of gradual departure from past norms.We analyze the time series data in the presence of trend,using Cox-Stuart methods together with the change point algorithms.We applied the methods to the nearsurface wind speed time series for Australia as an example.The trends in near-surface wind speeds for Australia have been investigated based upon our newly developed wind speed datasets,which were constructed by blending observational data collected at various heights using local surface roughness information.The trend in wind speed at 10 m is generally increasing while at 2 m it tends to be decreasing.Significance testing,change point analysis and manual inspection of records indicate several factors may be contributing to the discrepancy,such as systematic biases accompanying instrument changes,random data errors(e.g.accumulation day error)and data sampling issues.Homogenization technique and multiple-period trend analysis based upon change point detections have thus been employed to clarify the source of the inconsistencies in wind speed trends.
基金supported by the National Natural Science Foundation of China under Grant 62301119。
文摘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.
基金This work is supported by the National Key Research and Development Program of China(2022YFF1203001)National Natural Science Foundation of China(Nos.62072465,62102425)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘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.
基金The Key Project of National Natural Science Foundation of China(42130510)。
文摘Taking Shanghai as an example,this study obtained the online travel notes data from Xiaohongshu and Qunar in the past 10 years to construct the Shanghai tourist flow network(STFN)and used the methods of change point detection(CPD)and complex network analysis(CNA)to reveal the spatial structure characteristics of Shanghai tourism flow and the dynamic evolution process of STFN.The results showed that:(1)In the past 10 years,Shanghai tourist market had experienced a process of evolution from stable and orderly to short-term fluc-tuation and then gradual recovery,and the year of 2019 was the turning point of tourist flow network evolution.(2)The small-world and approximate scale-free characteristics of STFN were verified,and the network changed from disassortative to temporary assortative,showing a development trend of external expansion and internal separation.(3)While the centrality indicators of tourist flow network remained stable as a whole,the attention to cultural nodes was also increasing with the emergence of new nodes;(4)In terms of spatial connection,new popular nodes emerged and the relationship between them and the surrounding nodes was strengthened;(5)The spatial pattern of tourist flow network presented an inverted“V”shape and gradually expanded to southwest and southeast,forming a network with core nodes as the center and radiating outward.At the same time,newly emerging nodes at the periphery had formed relatively independent clusters.
基金This project is supported by the National Natural Science Foundation of Chinaby the Air Office of Scientific Research of the United States
文摘In this paper,the authors consider the problem of change points within the framework of model selection and propose a procedure for estimating the locations of change points when the number of change points is known.The strong consistency of this procedure is also established. The problem of detecting change points is discussed within the framework of the simultaneous test procedure.The case where the number of change points is unknown will be discussed in another paper.
文摘The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.
基金supported by the Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences[E429020101].
文摘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℃/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.
