Rolling forecasting forward by boosting heterogeneous kernels

Di Zhang*, Yunquan Zhang, Qiang Niu, Xingbao Qiu

*Corresponding author for this work

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

1 Citation (Scopus)


The problem discussed in this paper stems from a project of cellular network traffic prediction, the primary step of network planning striving to serve the continuously soaring network traffic best with limited resource. The traffic prediction emphasizes two aspects: (1) how to exploit the potential value of physical and electronic properties for tens of thousands of wireless stations, which may partly determine the allocation of traffic load in some intricate way; (2) the lack of sufficient and high-quality historical records, for the appropriate training of long-term predictions, further aggravated by frequent reconfigurations in daily operation. To solve this problem, we define a general framework to accommodate several variants of multi-step forecasting, via decomposing the problem into a series of single-step vector-output regression tasks. They can further be augmented by miscellaneous attributive information, in the form of boosted multiple kernels. Experiments on multiple telecom datasets show that the solution outperforms conventional time series methods on accuracy, especially for long horizons. Those attributes describing the macroscopic factors, such as the network type, topology, locations, are significantly helpful for longer horizons, whereas the immediate values in the near future are mainly determined by their recent records.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsDinh Phung, Geoffrey I. Webb, Bao Ho, Vincent S. Tseng, Mohadeseh Ganji, Lida Rashidi
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319930336
Publication statusPublished - 2018
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10937 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018


  • Multi-dimensional time series
  • Multi-horizon prediction
  • Multi-kernel learning
  • Network traffic prediction


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