一种不确定连续时间序列的Top-◢K◣异常检测算法OA北大核心CSCDCSTPCD
Uncertain continuous time series Top-◢K◣ anomaly detection method
针对噪声数据对时间序列异常检测准确性的影响问题，提出了一种不确定连续时间序列Top-◢K◣异常检测算法。在典型时间序列异常检测方法的基础上对时间序列的异常值进行区间处理，构造满足均匀分布的密度函数，结合不确定Top-◢K◣技术，实现含噪连续时间序列在分布未知情况下的Top-◢K◣异常排序。实验部分采用模拟数据和真实数据进行算法测试，算法较传统方法在异常检测的准确率方面有明显提高，虽然在计算时间上有所增加，但提出了相应的优化策略，使计算时间在◢K◣…查看全部>>
Aimed at the problem that the noise data influence on the anomaly detection results for time series， this paper put forward a kind of uncertain continuous time series Top-K anomaly detection algorithm. Based on the typical time series anomaly detection
MENG Fan-rong;YAO Yan-xu;CHANG Yu-hu;YAN Qiu-yan
School of Computer Science & Technology，China University of Mining & Technology，Xuzhou Jiangsu 221116，ChinSchool of Computer Science & Technology，China University of Mining & Technology，Xuzhou Jiangsu 221116，ChinSchool of Computer Science & Technology，China University of Mining & Technology，Xuzhou Jiangsu 221116，ChinSchool of Computer Science & Technology，China University of Mining & Technology，Xuzhou Jiangsu 221116，Chin
信息技术与安全科学
连续时间序列异常检测不确定数据Top-◢K◣排序
continuous time seriesanomaly detectionuncertain dataTop-◢K◣ ranking
《计算机应用研究》 2014 (3)
765-768,4
国家“863”计划资助项目(2012AA011004)国家级大学生科研训练计划资助项目(201210290076)国家教育部博士点基金资助项目(20110095110010)国家自然科学基金煤炭联合基金重点项目(U1261201)
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