Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

Chaoyun Zhang, Paul Patras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning and propose a Spatio-Temporal neural Network (STN) architecture purposely designed for precise network-wide mobile traffic forecasting. We present a mechanism that fine tunes the STN and enables its operation with only limited ground truth observations. We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections. Experiments we conduct with real-world mobile traffic data sets, collected over 60 days in both urban and rural areas, demonstrate that the proposed (D-)STN schemes perform up to 10-hour long predictions with remarkable accuracy, irrespective of the time of day when they are triggered. Specifically, our solutions achieve up to 61% smaller prediction errors as compared to widely used forecasting approaches, while operating with up to 600 times shorter measurement intervals.
Original languageEnglish
Title of host publicationThe Nineteenth International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2018)
Place of PublicationLos Angeles, CA, USA
PublisherACM
Pages231-240
Number of pages10
ISBN (Print)978-1-4503-5770-8
DOIs
Publication statusPublished - 26 Jun 2018
Event19th International Symposium on Mobile Ad Hoc Networking and Computing - University of California, Los Angeles Campus, Los Angeles , United States
Duration: 26 Jun 201829 Jun 2018
https://www.sigmobile.org/mobihoc/2018/

Conference

Conference19th International Symposium on Mobile Ad Hoc Networking and Computing
Abbreviated titleMobiHoc 2018
Country/TerritoryUnited States
CityLos Angeles
Period26/06/1829/06/18
Internet address

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