{"title":"深度或统计:对多个时间尺度的交通预测进行实证研究","authors":"Yu Qiao, Chengxiang Li, Shuzheng Hao, Junying Wu, Liang Zhang","doi":"10.1145/3546037.3546048","DOIUrl":null,"url":null,"abstract":"Traffic prediction aims to forecast the future traffic level based on past observations. In this paper, we conduct an empirical study of traffic prediction for a campus trace on different time scales and get the following conclusions: 1) deep learning performs well on coarser time scales; 2) with a finer-granularity of time or insufficient data, statistical and regressive models outperform; 3) For a one-week trace, the granularity of 5 minutes has the strongest predictability.","PeriodicalId":351682,"journal":{"name":"Proceedings of the SIGCOMM '22 Poster and Demo Sessions","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Deep or statistical: an empirical study of traffic predictions on multiple time scales\",\"authors\":\"Yu Qiao, Chengxiang Li, Shuzheng Hao, Junying Wu, Liang Zhang\",\"doi\":\"10.1145/3546037.3546048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traffic prediction aims to forecast the future traffic level based on past observations. In this paper, we conduct an empirical study of traffic prediction for a campus trace on different time scales and get the following conclusions: 1) deep learning performs well on coarser time scales; 2) with a finer-granularity of time or insufficient data, statistical and regressive models outperform; 3) For a one-week trace, the granularity of 5 minutes has the strongest predictability.\",\"PeriodicalId\":351682,\"journal\":{\"name\":\"Proceedings of the SIGCOMM '22 Poster and Demo Sessions\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the SIGCOMM '22 Poster and Demo Sessions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3546037.3546048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the SIGCOMM '22 Poster and Demo Sessions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3546037.3546048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep or statistical: an empirical study of traffic predictions on multiple time scales
Traffic prediction aims to forecast the future traffic level based on past observations. In this paper, we conduct an empirical study of traffic prediction for a campus trace on different time scales and get the following conclusions: 1) deep learning performs well on coarser time scales; 2) with a finer-granularity of time or insufficient data, statistical and regressive models outperform; 3) For a one-week trace, the granularity of 5 minutes has the strongest predictability.
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