计算机与数字工程2018，Vol.46Issue(2)：225-230,6.DOI:10.3969/j.issn.1672-9722.2018.02.004
基于ARIMA-SVR的水文时间序列异常值检测
Outlier Detection of Hydrological Time Series Based on ARIMA-SVR Model
摘要
Abstract
Due to weather,terrain and other complex factors,hydrological data usually have many unusual values,and it has an important impact on decision-making.In this paper,an anomaly detection algorithm based on ARIMA-SVR is proposed,and it improves the quality of anomaly detection.First,the ARIMA model is used to predict the linear autocorrelation of the hydrological time series,and then the SVR-model is used to predict the nonlinear part.Secondly,the predicted results are summed up and the confidence intervals of confidence P is obtained.At last,the actual value which is not within the confidence interval is an outlier. We use The Liuhe hydrological station measured data is used to verify,and experimental results show that the proposed algorithm can effectively detect the outliers in the hydrological time series,while the specificity and sensitivity are maintained at a high level.关键词
水文时间序列/异常检测/ARIMA模型/支持向量回归Key words
hydrological time series/anomaly detection/ARIMA-model/SVR分类
信息技术与安全科学引用本文复制引用
孙建树,娄渊胜,陈裕俊..基于ARIMA-SVR的水文时间序列异常值检测[J].计算机与数字工程,2018,46(2):225-230,6.基金项目
国家自然科学基金项目(编号:61300122) （编号:61300122）
2013年江苏水利科技项目(编号:2013025)资助. （编号:2013025）
