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
A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochast...A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined （DSC） approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average （SARIMA） models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term （10-year） environmental planning and decision making.展开更多
For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself ...For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.展开更多
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept...In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.展开更多
针对传统模型在机组负荷预测中无法充分捕获内部多变量演化模式的问题,提出了一种基于时间序列的趋势和数值信息融合的双重回声状态网络Dual-ESN(dual-echo state network)机组负荷动态预测模型。首先,引入最小二乘法,对相关的多元历史...针对传统模型在机组负荷预测中无法充分捕获内部多变量演化模式的问题,提出了一种基于时间序列的趋势和数值信息融合的双重回声状态网络Dual-ESN(dual-echo state network)机组负荷动态预测模型。首先,引入最小二乘法,对相关的多元历史信息按照局部时间跨度进行趋势拟合。进一步,得到有关过程变化的模式序列,并和原本的数值分别被送入两个独立的储备池,以并行的时间维度进行特征学习。其次,将隐层的高维空间状态送入输出层,融合信息,得到所需要的预测结果。最后,基于山西某工厂660 MW机组装置的真实数据集,进行验证。对比已有预测方法,结果表明所提预测模型在多种性能指标上均有提升。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
时间序列的近似表示和相似度量是时间序列数据挖掘的重要任务之一,是进行相似匹配的关键。该文针对现有的各种基于分段线性表示(Piecewise Linear Representation,PLR)相似度量方法存在的序列长度依赖和多分辨率条件下的潜在识别误差等...时间序列的近似表示和相似度量是时间序列数据挖掘的重要任务之一,是进行相似匹配的关键。该文针对现有的各种基于分段线性表示(Piecewise Linear Representation,PLR)相似度量方法存在的序列长度依赖和多分辨率条件下的潜在识别误差等缺点,提出了一种序列分段线性弧度表示和基于弧度距离的相似度量方法,实现了序列的快速在线分割和相似度计算。该方法简洁直观,利用分段弧度对分段趋势进行细粒度划分来保留序列主要形态特征,有效地提高了度量结果的准确性和多分辨率条件下的稳定性。该方法具有序列分割算法独立性特点,可用于时间序列的相似查询、模式匹配、分类和聚类。展开更多
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 research was supported by the Ministry of Science and Technology of China，National Basic Research Program of China (Grant No.2010CB951504).The authors acknowledge support from the Flemish Interuniversity Council，the Ghent University Laboratory of Soil Science for the writing of this paper
文摘A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined （DSC） approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average （SARIMA） models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term （10-year） environmental planning and decision making.
基金funded by Prince Sultan University,Riyadh,Saudi Arabia。
文摘For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.
基金Project(08SK1002) supported by the Major Project of Science and Technology Department of Hunan Province,China
文摘In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.
文摘针对传统模型在机组负荷预测中无法充分捕获内部多变量演化模式的问题,提出了一种基于时间序列的趋势和数值信息融合的双重回声状态网络Dual-ESN(dual-echo state network)机组负荷动态预测模型。首先,引入最小二乘法,对相关的多元历史信息按照局部时间跨度进行趋势拟合。进一步,得到有关过程变化的模式序列,并和原本的数值分别被送入两个独立的储备池,以并行的时间维度进行特征学习。其次,将隐层的高维空间状态送入输出层,融合信息,得到所需要的预测结果。最后,基于山西某工厂660 MW机组装置的真实数据集,进行验证。对比已有预测方法,结果表明所提预测模型在多种性能指标上均有提升。
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.
文摘时间序列的近似表示和相似度量是时间序列数据挖掘的重要任务之一,是进行相似匹配的关键。该文针对现有的各种基于分段线性表示(Piecewise Linear Representation,PLR)相似度量方法存在的序列长度依赖和多分辨率条件下的潜在识别误差等缺点,提出了一种序列分段线性弧度表示和基于弧度距离的相似度量方法,实现了序列的快速在线分割和相似度计算。该方法简洁直观,利用分段弧度对分段趋势进行细粒度划分来保留序列主要形态特征,有效地提高了度量结果的准确性和多分辨率条件下的稳定性。该方法具有序列分割算法独立性特点,可用于时间序列的相似查询、模式匹配、分类和聚类。
