S. Jeon, Seongman Min, Dawoon Chung, Kangsoo Kim, Jahoon Ku, Sunhyung Kwon, Sung-Ik Park
{"title":"Simple Anomaly Detection Technique from Long-term Time-series Data by ATSC 3.0 Single Frequency Broadcast Network Monitoring System","authors":"S. Jeon, Seongman Min, Dawoon Chung, Kangsoo Kim, Jahoon Ku, Sunhyung Kwon, Sung-Ik Park","doi":"10.1109/BMSB58369.2023.10211288","DOIUrl":null,"url":null,"abstract":"Long-term Time Series Data from ATSC 3.0 single frequency broadcast network operation is important to understand anomaly patterns by measurement metrics because it provides a comprehensive view of the performance and behavior of the ATSC 3.0 network over time. This information can help identify and analyze patterns, trends, and outliers in the data, which can provide valuable insights into the health and stability of the network. By understanding these anomaly patterns, engineers and technicians can improve the network’s performance, troubleshoot issues, and make informed decisions about future upgrades and maintenance.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"14 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMSB58369.2023.10211288","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Long-term Time Series Data from ATSC 3.0 single frequency broadcast network operation is important to understand anomaly patterns by measurement metrics because it provides a comprehensive view of the performance and behavior of the ATSC 3.0 network over time. This information can help identify and analyze patterns, trends, and outliers in the data, which can provide valuable insights into the health and stability of the network. By understanding these anomaly patterns, engineers and technicians can improve the network’s performance, troubleshoot issues, and make informed decisions about future upgrades and maintenance.