一种基于 ARIMA 模型与 3σ 准则的取水异常检测方法

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

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 3 σ 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 3 σ 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，并将模型在真实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。

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 monitoringof water intake and improve the data reliability.