Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. Fi...Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.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
Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time...Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time series.Firstly,an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation).On the basis of considering trajectory coverage,this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation.Then the compression efficiency of typical offline and online algorithms,such as the Douglas-Peucker algorithm,the Sliding Window algorithm and its enhancements,was compared before and after segmentation.The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation.Additionally,the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales,providing reasonable compression accuracy.展开更多
Multivariate time series segmentation is an important problem in data mining and it has arisen in more and more practical applications in recent years.The task of time series segmentation is to partition a time series...Multivariate time series segmentation is an important problem in data mining and it has arisen in more and more practical applications in recent years.The task of time series segmentation is to partition a time series into segments by detecting the abrupt changes or anomalies in the time series.Multivariate time series segmentation can provide meaningful information for further data analysis,prediction and policy decision.A time series can be considered as a piecewise continuous function,it is natural to take its total variation norm as a prior information of this time series.In this paper,by minimizing the negative log-likelihood function of a time series,we propose a total variation based model for multivariate time series segmentation.An iterative process is applied to solve the proposed model and a search combined the dynamic programming method is designed to determine the breakpoints.The experimental results show that the proposed method is efficient for multivariate time series segmentation and it is competitive to the existing methods for multivariate time series segmentation.展开更多
智慧工地的高层建筑塔吊安全是在建筑行业亟待解决的关键问题之一,塔身倾斜度是塔吊运动控制中的一个重要监测指标,为解决塔吊倾角预测精度不高问题,提出了残差学习(Res-Net)-双向长短期记忆神经网络(Bi-directional Long Short-Term Me...智慧工地的高层建筑塔吊安全是在建筑行业亟待解决的关键问题之一,塔身倾斜度是塔吊运动控制中的一个重要监测指标,为解决塔吊倾角预测精度不高问题,提出了残差学习(Res-Net)-双向长短期记忆神经网络(Bi-directional Long Short-Term Memory)模型预测高层建筑塔吊塔身各段倾角的方法.以分段监测的塔身倾角为输入,对塔吊塔身各段倾角实时监测预测.采用鲸鱼算法对模型进行优化,以最小化Res-Bi-LSTM网络的均方根误差为目标,寻找最优超参数,使得网络的误差最小.最终实现对塔身各段倾角的有效预测.实验结果分析提出的模型均方根误差(RMSE)降低到0.8%,模型的拟合优度达到94.96%,均优于对比实验的RNN、Bi-LSTM模型.本文所提出的模型具有更高的预测精度.展开更多
The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution...The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation （TFSA）. The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.展开更多
文摘Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.
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Supported by the Basic Research Projects of Liaoning Provincial Department of Education(LJKQZ20222459)。
文摘Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time series.Firstly,an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation).On the basis of considering trajectory coverage,this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation.Then the compression efficiency of typical offline and online algorithms,such as the Douglas-Peucker algorithm,the Sliding Window algorithm and its enhancements,was compared before and after segmentation.The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation.Additionally,the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales,providing reasonable compression accuracy.
基金This work is supported by the National Natural Science Foundation of China Nos.11971215,11871210,and 11971214the Key Laboratory of Applied Mathematics and Complex Systems of Lanzhou University.
文摘Multivariate time series segmentation is an important problem in data mining and it has arisen in more and more practical applications in recent years.The task of time series segmentation is to partition a time series into segments by detecting the abrupt changes or anomalies in the time series.Multivariate time series segmentation can provide meaningful information for further data analysis,prediction and policy decision.A time series can be considered as a piecewise continuous function,it is natural to take its total variation norm as a prior information of this time series.In this paper,by minimizing the negative log-likelihood function of a time series,we propose a total variation based model for multivariate time series segmentation.An iterative process is applied to solve the proposed model and a search combined the dynamic programming method is designed to determine the breakpoints.The experimental results show that the proposed method is efficient for multivariate time series segmentation and it is competitive to the existing methods for multivariate time series segmentation.
文摘智慧工地的高层建筑塔吊安全是在建筑行业亟待解决的关键问题之一,塔身倾斜度是塔吊运动控制中的一个重要监测指标,为解决塔吊倾角预测精度不高问题,提出了残差学习(Res-Net)-双向长短期记忆神经网络(Bi-directional Long Short-Term Memory)模型预测高层建筑塔吊塔身各段倾角的方法.以分段监测的塔身倾角为输入,对塔吊塔身各段倾角实时监测预测.采用鲸鱼算法对模型进行优化,以最小化Res-Bi-LSTM网络的均方根误差为目标,寻找最优超参数,使得网络的误差最小.最终实现对塔身各段倾角的有效预测.实验结果分析提出的模型均方根误差(RMSE)降低到0.8%,模型的拟合优度达到94.96%,均优于对比实验的RNN、Bi-LSTM模型.本文所提出的模型具有更高的预测精度.
基金supported by the National High-Tech R&D Program(863)of China(Nos.2012AA012600,2011AA010702,2012AA01A401,and 2012AA01A402)the National Natural Science Foundation of China(No.60933005)the National Science and Technology of China(No.2012BAH38B04)
文摘The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation （TFSA）. The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.
