赵和松1，王圆圆2，孙爱民3

　　(1.水利部信息中心，北京 100053)

　　(2.北京金水信息技术发展有限公司，北京 100053)

　　(3.河海大学计算机与信息学院，江苏 南京 211100)

　　摘　要：为提高取水预测数据的准确性，针对现有部分取水数据异常且难以进行人工判别的问题，提出一种基于ARIMA模型与3σ准则的取水异常检测方法。分析每个取水点每年的日取水量的时间序列数据，使用时间序列的ARIMA模型和高斯分布的3σ准则判断日取水量是否为异常值;通过时间序列分解算法分析异常值附近取水点的趋势，判断异常值附近是否存在其他未检测出的异常值，给出异常值的参考修正值。对所提模型在带异常标签的通用时间序列数据集上进行实验，通过评价指标混淆矩阵验证模型可行性，并将模型在水利部门取水数据集上进行实验，结果表明：模型可有效检测取水数据中的异常值并修正其值，对取水异常的原因进行分析有助于改进取用水的采集方法，提高取水监测数据的质量。

　　关键词： 取水异常检测;机器学习;ARIMA模型;3σ准则;时间序列分解算法

　　A water intake anomaly detection method based on ARIMA model and 3σ criterion

　　ZHAO Hesong1，WANG Yuanyuan2，SUN Aimin3

　　(1.Information Center, Ministry of Water Resources，Beijing 100053，China;

　　2. Beijing Jinshui Information Technology Development Co.，Ltd.，Beijing 100053，China;

　　3. School of computer and information，Hohai University，Nanjing 211100，China)

　　Abstract: In order to improve the accuracy of water intake prediction data, a water intake anomaly detection method based on ARIMA model and 3σ criterion is proposed. Some existing daily water intake data is abnormal and difficult to be manually distinguished. This paper analyzes the time series data for each water intake point, and then applies the ARIMA model of time series and the 3σ criterion of Gaussian distribution to check the outliers. The decomposition algorithm is used to analyze the trend of time series near outliers, and distinguish undetected outliers and determine their values. Experiments on the proposed model on a universal time series dataset with anomalous labels are carried out. The feasibility of the model is verified through the evaluation index confusion matrix. The results show that the model can effectively detect the outliers in water intake data and provide reference values. The analysis of the causes of water intake anomalies is helpful to improve the monitoring of water intake and improve the data reliability.

　　Key words: water intake anomaly detection;machine learning;ARIMA model;3σ criteria; decomposition algorithm of time series