poster

Deep or statistical: an empirical study of traffic predictions on multiple time scales

Authors:

Yu Qiao,

Chengxiang Li,

Shuzheng Hao,

Jun Wu,

Liang ZhangAuthors Info & Claims

SIGCOMM '22: Proceedings of the SIGCOMM '22 Poster and Demo Sessions

Pages 10 - 12

https://doi.org/10.1145/3546037.3546048

Published: 25 October 2022 Publication History

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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.

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R. F. Engle. 1981. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of U.K. Inflation. In Econometrica, Vol. 50. 987--1008.

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Lai Guokun, Chang Wei Cheng, Yang Yiming, and Liu Hanxiao. 2018. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. ACM (2018).

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Richman Joshua S. and J. Randall Moorman. 2000. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology (2000).

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Q. Yi, J. Skicewicz, and P. Dinda. 2004. An empirical study of the multiscale predictability of network traffic. In IEEE International Symposium on High Performance Distributed Computing.

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Cited By

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Tang BMo YYu CLiu H(2025)End-to-End Steady-State Adaptive Slicing Method for Dynamic Network State and LoadIEEE Transactions on Mobile Computing10.1109/TMC.2024.347390824:2(1090-1104)Online publication date: Feb-2025
https://doi.org/10.1109/TMC.2024.3473908
Di Cicco NAl Sadi AGrasselli CMelis AAntichi GTornatore MSchulzrinne HKohler EMaltz DMisra V(2023)Poster: Continual Network LearningProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3610855(1096-1098)Online publication date: 10-Sep-2023
https://dl.acm.org/doi/10.1145/3603269.3610855

Index Terms

Deep or statistical: an empirical study of traffic predictions on multiple time scales
1. Networks
  1. Network performance evaluation
    1. Network performance modeling

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Published In

SIGCOMM '22: Proceedings of the SIGCOMM '22 Poster and Demo Sessions

August 2022

69 pages

ISBN:9781450394345

DOI:10.1145/3546037

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 October 2022

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SIGCOMM '22: ACM SIGCOMM 2022 Conference

August 22 - 26, 2022

Amsterdam, Netherlands

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Overall Acceptance Rate 92 of 158 submissions, 58%

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Tang BMo YYu CLiu H(2025)End-to-End Steady-State Adaptive Slicing Method for Dynamic Network State and LoadIEEE Transactions on Mobile Computing10.1109/TMC.2024.347390824:2(1090-1104)Online publication date: Feb-2025
https://doi.org/10.1109/TMC.2024.3473908
Di Cicco NAl Sadi AGrasselli CMelis AAntichi GTornatore MSchulzrinne HKohler EMaltz DMisra V(2023)Poster: Continual Network LearningProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3610855(1096-1098)Online publication date: 10-Sep-2023
https://dl.acm.org/doi/10.1145/3603269.3610855

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Recommendations

Long-time gap crowd prediction using time series deep learning models with two-dimensional single attribute inputs

A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia

MiST: Enhancing Traffic Predictions with a Mixing Spatio-temporal Neural Network

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