Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Burst Detection by Analyzing Shape Similarity of Time Series Subsequences in District Metering Areas
The paper proposes a burst detection method that relies on shape similarity analysis of time series subsequences (i.e., slices of time series). Subsequence libraries are constructed using flow (or water demand) data. Increase-rate distance is used to evaluate the shape similarity between subsequences, and abnormal subsequences are those that have low shape similarity with others. An abnormal subsequence searching algorithm first is used to remove abnormal subsequences, and the remaining subsequences are used to form reference libraries. Then the shape similarity between newly collected subsequences and reference libraries is evaluated to detect bursts. In the detection, a modified version of the abnormal subsequence searching algorithm can reduce the number of false alarms by finding the don’t-care segment in subsequences and improve the method’s detection ability by crossover between night subsequences. The method was applied to a network’s hydraulic model and three real-life district metering areas. Results show that the method’s detection performance is only slightly affected by seasonal changes of data and is insensitive to data sets from different networks.
Burst Detection by Analyzing Shape Similarity of Time Series Subsequences in District Metering Areas
The paper proposes a burst detection method that relies on shape similarity analysis of time series subsequences (i.e., slices of time series). Subsequence libraries are constructed using flow (or water demand) data. Increase-rate distance is used to evaluate the shape similarity between subsequences, and abnormal subsequences are those that have low shape similarity with others. An abnormal subsequence searching algorithm first is used to remove abnormal subsequences, and the remaining subsequences are used to form reference libraries. Then the shape similarity between newly collected subsequences and reference libraries is evaluated to detect bursts. In the detection, a modified version of the abnormal subsequence searching algorithm can reduce the number of false alarms by finding the don’t-care segment in subsequences and improve the method’s detection ability by crossover between night subsequences. The method was applied to a network’s hydraulic model and three real-life district metering areas. Results show that the method’s detection performance is only slightly affected by seasonal changes of data and is insensitive to data sets from different networks.
Burst Detection by Analyzing Shape Similarity of Time Series Subsequences in District Metering Areas
Wu, Yipeng (Autor:in) / Liu, Shuming (Autor:in)
31.10.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
| DOAJ | 2024
Distance-Based Burst Detection Using Multiple Pressure Sensors in District Metering Areas
| British Library Online Contents | 2018
| DOAJ | 2023