针对时序数据挖掘中传统趋势序列分析的缺点,提出了数字趋势序列、趋势序列展开等概念.根据数字趋势序列的特点,使用片段斜率所对应的弧度值来度量片段的趋势.针对数字趋势序列的子序列匹配问题,设计了DTW(Dynamic Time Warping)快速搜...针对时序数据挖掘中传统趋势序列分析的缺点,提出了数字趋势序列、趋势序列展开等概念.根据数字趋势序列的特点,使用片段斜率所对应的弧度值来度量片段的趋势.针对数字趋势序列的子序列匹配问题,设计了DTW(Dynamic Time Warping)快速搜索算法.算法分为3个部分:DTW顺序搜索、约束机制、冗余消除机制.并使用实际的股票数据对算法进行了验证.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
文摘针对时序数据挖掘中传统趋势序列分析的缺点,提出了数字趋势序列、趋势序列展开等概念.根据数字趋势序列的特点,使用片段斜率所对应的弧度值来度量片段的趋势.针对数字趋势序列的子序列匹配问题,设计了DTW(Dynamic Time Warping)快速搜索算法.算法分为3个部分:DTW顺序搜索、约束机制、冗余消除机制.并使用实际的股票数据对算法进行了验证.
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.
