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首页> 外文期刊>Journal of Hydroinformatics >Novelty detection for time series data analysis in water distribution systems using support vector machines
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Novelty detection for time series data analysis in water distribution systems using support vector machines

机译：使用支持向量机的水分配系统中时间序列数据分析的新颖性检测

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摘要

The sampling frequency and quantity of time series data collected from water distribution systems has been increasing in recent years, giving rise to the potential for improving system knowledge if suitable automated techniques can be applied, in particular, machine learning. Novelty (or anomaly) detection refers to the automatic identification of novel or abnormal patterns embedded in large amounts of "normal" data. When dealing with time series data (transformed into vectors), this means abnormal events embedded amongst many normal time series points. The support vector machine is a data-driven statistical technique that has been developed as a tool for classification and regression. The key features include statistical robustness with respect to non-Gaussian errors and outliers, the selection of the decision boundary in a principled way, and the introduction of nonlinearity in the feature space without explicitly requiring a nonlinear algorithm by means of kernel functions, in this research, support vector regression is used as a learning method for anomaly detection from water flow and pressure time series data. No use is made of past event histories collected through other information sources. The support vector regression methodology, whose robustness derives from the training error function, is applied to a case study.
机译：从水分配系统收集的时间序列数据的采样频率和数量近年来一直在增加，如果可以应用合适的自动化技术（尤其是机器学习），则有可能提高系统知识。新颖性（或异常）检测是指自动识别嵌入大量“正常”数据中的新颖或异常模式。当处理时间序列数据（转换为矢量）时，这意味着在许多正常时间序列点中嵌入了异常事件。支持向量机是一种数据驱动的统计技术，已被开发为用于分类和回归的工具。关键特征包括针对非高斯误差和离群值的统计鲁棒性，以有原则的方式选择决策边界以及在特征空间中引入非线性而无需通过内核函数明确要求非线性算法的情况在研究中，支持向量回归被用作从水流量和压力时间序列数据中异常检测的学习方法。没有利用通过其他信息源收集的过去事件历史记录。支持向量回归方法的稳健性来自训练误差函数，该方法应用于案例研究。

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