Telecommunication Traffic Forecasting via Multi-task Learning

Xiaochuan Gou, Xiangliang Zhang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Accurate telecommunication time series forecasting is critical for smart management systems of cellular networks, and has a special challenge in predicting different types of time series simultaneously at one base station (BS), e.g., the SMS, Calls, and Internet. Unlike the well-studied single target forecasting problem for one BS, this distributed multi-target forecasting problem should take advantage of both the intra-BS dependence of different types of time series at the same BS and the inter-BS dependence of time series at different BS. To this end, we first propose a model to learn the inter-BS dependence by aggregating the multi-view dependence, e.g., from the viewpoint of SMS, Calls, and Internet. To incorporate the interBS dependence in time series forecasting, we then propose a Graph Gate LSTM (GGLSTM) model that includes a graph-based gate mechanism to unite those base stations with a strong dependence on learning a collaboratively strengthened prediction model. We also extract the intra-BS dependence by an attention network and use it in the final prediction. Our proposed approach is evaluated on two real-world datasets. Experiment results demonstrate the effectiveness of our model in predicting multiple types of telecom traffic at the distributed base stations.

Original languageEnglish (US)
Title of host publicationWSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages859-867
Number of pages9
ISBN (Electronic)9781450394079
DOIs
StatePublished - Feb 27 2023
Event16th ACM International Conference on Web Search and Data Mining, WSDM 2023 - Singapore, Singapore
Duration: Feb 27 2023Mar 3 2023

Publication series

NameWSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining

Conference

Conference16th ACM International Conference on Web Search and Data Mining, WSDM 2023
Country/TerritorySingapore
CitySingapore
Period02/27/2303/3/23

Bibliographical note

Funding Information:
The research reported in this paper was supported by funding from King Abdullah University of Science and Technology (KAUST).

Publisher Copyright:
© 2023 ACM.

Keywords

  • multi-source time series forecasting
  • multi-task learning
  • telecommunication traffic forecasting

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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