Deep or statistical: an empirical study of traffic predictions on multiple time scales
Y Qiao, C Li, S Hao, J Wu, L Zhang - … of the SIGCOMM'22 Poster and …, 2022 - dl.acm.org
Y Qiao, C Li, S Hao, J Wu, L Zhang
Proceedings of the SIGCOMM'22 Poster and Demo Sessions, 2022•dl.acm.orgTraffic 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.
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.
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.