This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained u...This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained under suitable conditions. Finally, the author shows that the usual weight functions based on nearest neighbor methods satisfy the designed assumptions imposed.展开更多
Consumption of the electric power highly depends on the Season under consideration. The various means of power generation methods using renewable resources such as sunlight, wind, rain, tides, and waves are season dep...Consumption of the electric power highly depends on the Season under consideration. The various means of power generation methods using renewable resources such as sunlight, wind, rain, tides, and waves are season dependent. This paves the way for analyzing the demand for electric power based on various Seasons. Many traditional methods are utilized previously for the seasonal based electricity demand forecasting. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. In this paper, a WEKA time series forecasting is being done for the electric power demand for the three seasons such as summer, winter and rainy seasons. The monthly electric consumption data of domestic category is collected from Tamil Nadu Electricity Board (TNEB). Data collected has been pruned based on the three seasons. The WEKA learning algorithms such as Multilayer Perceptron, Support Vector Machine, Linear Regression, and Gaussian Process are used for implementation. The Mean Absolute Error (MAE) and Direction Accuracy (DA) are calculated for the WEKA learning algorithms and they are compared to find the best learning algorithm. The Support Vector Machine algorithm exhibits low Mean Absolute Error and high Direction Accuracy than other WEKA learning algorithms. Hence, the Support Vector Machine learning algorithm is proven to be the WEKA learning algorithm for seasonal based electricity demand forecasting. The need of the hour is to predict and act in the deficit power. This paper is a prelude for such activity and an eye opener in this field.展开更多
针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟...针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。展开更多
随着全球定位系统的发展和应用,巨量的轨迹数据被实时收集,给数据的传输、存储和分析带来挑战.基于分段线性近似(piecewise linear approximation,PLA)的数据压缩技术因具有简单直观、压缩存储低和传输快的特点被广泛应用和研究.针对现...随着全球定位系统的发展和应用,巨量的轨迹数据被实时收集,给数据的传输、存储和分析带来挑战.基于分段线性近似(piecewise linear approximation,PLA)的数据压缩技术因具有简单直观、压缩存储低和传输快的特点被广泛应用和研究.针对现有轨迹PLA压缩方法不能最优化地在线压缩多维数据的现状,在最大误差限定(maximum error bound,记为L_(∞))下提出多维轨迹数据的最优化PLA压缩问题(记为m DisPLA_(∞)),并给出一种在线MDisPLA算法予以解决.该算法利用“分治-融合”的策略扩展一维最优化PLA算法,以最优化地压缩多维轨迹数据.MDisPLA算法具有线性时间复杂性,可以生成最少的不连续分割,且可以保证生成直线表示的质量,即原始数据点和对应解压缩点之间的同步误差具有上界.通过与基于同步距离锥交(cone intersection using the synchronous Euclidean distance,CISED)的轨迹压缩算法进行理论和实验比较,验证了MDisPLA算法是稳健的,可生成具有保质性的直线表示.MDisPLA算法以更低的内存消耗,较CISED算法提高了14倍左右的处理速度,降低了约48%的分割个数和10.5%的存储个数.MDisPLA算法在保证压缩质量的同时,显著提高了处理速度和降低了存储空间,整体上优于CISED算法.展开更多
针对在大规模时序医疗数据的分析中现有检测方法检测精度低、检测速度慢等问题,文中提出了一种基于深度学习的时序病变数据段分类方法。该方法在TSTKS(Ternary Search Trees and modified Kolmogorov-Smirnov)算法和滑动窗口理论的基础...针对在大规模时序医疗数据的分析中现有检测方法检测精度低、检测速度慢等问题,文中提出了一种基于深度学习的时序病变数据段分类方法。该方法在TSTKS(Ternary Search Trees and modified Kolmogorov-Smirnov)算法和滑动窗口理论的基础上,利用深度学习技术实现了对病变数据段的快速准确分类。文中以利用该方法对病变数据段进行分类的结果作为依据,实现了滑动窗口大小的动态调整。通过对真实癫痫脑电信号(Electroencephalogram,EEG)进行分析,证明了所提病变数据段分类方法和基于该分类方法的滑动窗口动态调整机制具有检测速度快、精度较高等优点,可以为大规模时序数据的快速分析研究提供一种新选择。展开更多
In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using v...In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using various types of chromosomes is used.The first method is a GA with binary chromosome（GA-BC） and the other is a GA with a fixed-length character chromosome（GA-FCC）.The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression（FSR） model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability（R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675） using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.展开更多
为了解决传统自适应阈值算法对时间序列方差跟踪能力不足,以及故障阶段带宽自动放大的问题,提出了紧广义自回归条件异方差(Compact General Auto-Regressive Conditional Heteroskedasticity,CGARCH)模型。针对液体火箭发动机稳态试车...为了解决传统自适应阈值算法对时间序列方差跟踪能力不足,以及故障阶段带宽自动放大的问题,提出了紧广义自回归条件异方差(Compact General Auto-Regressive Conditional Heteroskedasticity,CGARCH)模型。针对液体火箭发动机稳态试车数据的波动性特点,提出一种基于自回归(Auto-Regressive,AR)模型和CGARCH模型的自适应阈值故障检测算法。采用AR模型对稳态参数的均值进行估计,并采用CGARCH模型对稳态参数的方差进行估计,从而利用均值和方差的估计值自适应地构造检测阈值。用某氢氧火箭发动机的热试车数据进行验证,结果表明,该算法能够准确、快速、灵敏地检测液体火箭发动机故障,在正常工作阶段,能够有效跟踪数据波动性,在故障阶段,能够避免阈值变宽带来的漏检。展开更多
基金This article was supported by the National Natural Science Foundation of China(10571001)the Innovation Group Foundation of Anhui University
文摘This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained under suitable conditions. Finally, the author shows that the usual weight functions based on nearest neighbor methods satisfy the designed assumptions imposed.
文摘Consumption of the electric power highly depends on the Season under consideration. The various means of power generation methods using renewable resources such as sunlight, wind, rain, tides, and waves are season dependent. This paves the way for analyzing the demand for electric power based on various Seasons. Many traditional methods are utilized previously for the seasonal based electricity demand forecasting. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. In this paper, a WEKA time series forecasting is being done for the electric power demand for the three seasons such as summer, winter and rainy seasons. The monthly electric consumption data of domestic category is collected from Tamil Nadu Electricity Board (TNEB). Data collected has been pruned based on the three seasons. The WEKA learning algorithms such as Multilayer Perceptron, Support Vector Machine, Linear Regression, and Gaussian Process are used for implementation. The Mean Absolute Error (MAE) and Direction Accuracy (DA) are calculated for the WEKA learning algorithms and they are compared to find the best learning algorithm. The Support Vector Machine algorithm exhibits low Mean Absolute Error and high Direction Accuracy than other WEKA learning algorithms. Hence, the Support Vector Machine learning algorithm is proven to be the WEKA learning algorithm for seasonal based electricity demand forecasting. The need of the hour is to predict and act in the deficit power. This paper is a prelude for such activity and an eye opener in this field.
文摘针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。
文摘随着全球定位系统的发展和应用,巨量的轨迹数据被实时收集,给数据的传输、存储和分析带来挑战.基于分段线性近似(piecewise linear approximation,PLA)的数据压缩技术因具有简单直观、压缩存储低和传输快的特点被广泛应用和研究.针对现有轨迹PLA压缩方法不能最优化地在线压缩多维数据的现状,在最大误差限定(maximum error bound,记为L_(∞))下提出多维轨迹数据的最优化PLA压缩问题(记为m DisPLA_(∞)),并给出一种在线MDisPLA算法予以解决.该算法利用“分治-融合”的策略扩展一维最优化PLA算法,以最优化地压缩多维轨迹数据.MDisPLA算法具有线性时间复杂性,可以生成最少的不连续分割,且可以保证生成直线表示的质量,即原始数据点和对应解压缩点之间的同步误差具有上界.通过与基于同步距离锥交(cone intersection using the synchronous Euclidean distance,CISED)的轨迹压缩算法进行理论和实验比较,验证了MDisPLA算法是稳健的,可生成具有保质性的直线表示.MDisPLA算法以更低的内存消耗,较CISED算法提高了14倍左右的处理速度,降低了约48%的分割个数和10.5%的存储个数.MDisPLA算法在保证压缩质量的同时,显著提高了处理速度和降低了存储空间,整体上优于CISED算法.
文摘针对在大规模时序医疗数据的分析中现有检测方法检测精度低、检测速度慢等问题,文中提出了一种基于深度学习的时序病变数据段分类方法。该方法在TSTKS(Ternary Search Trees and modified Kolmogorov-Smirnov)算法和滑动窗口理论的基础上,利用深度学习技术实现了对病变数据段的快速准确分类。文中以利用该方法对病变数据段进行分类的结果作为依据,实现了滑动窗口大小的动态调整。通过对真实癫痫脑电信号(Electroencephalogram,EEG)进行分析,证明了所提病变数据段分类方法和基于该分类方法的滑动窗口动态调整机制具有检测速度快、精度较高等优点,可以为大规模时序数据的快速分析研究提供一种新选择。
基金supported by Youth Foundation of the Education Department of Sichuan Province (No.09ZB038)
文摘In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using various types of chromosomes is used.The first method is a GA with binary chromosome（GA-BC） and the other is a GA with a fixed-length character chromosome（GA-FCC）.The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression（FSR） model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability（R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675） using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.
文摘为了解决传统自适应阈值算法对时间序列方差跟踪能力不足,以及故障阶段带宽自动放大的问题,提出了紧广义自回归条件异方差(Compact General Auto-Regressive Conditional Heteroskedasticity,CGARCH)模型。针对液体火箭发动机稳态试车数据的波动性特点,提出一种基于自回归(Auto-Regressive,AR)模型和CGARCH模型的自适应阈值故障检测算法。采用AR模型对稳态参数的均值进行估计,并采用CGARCH模型对稳态参数的方差进行估计,从而利用均值和方差的估计值自适应地构造检测阈值。用某氢氧火箭发动机的热试车数据进行验证,结果表明,该算法能够准确、快速、灵敏地检测液体火箭发动机故障,在正常工作阶段,能够有效跟踪数据波动性,在故障阶段,能够避免阈值变宽带来的漏检。
