Subset selection using frequency decomposition with applications

Wai Man Tang, Ka Fai Cedric Yiu, Heung Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


In time series modeling, one problem is to identify a small number of influential factors to explain variations in the variable of interest. With a vast number of possible factors available, suitable features need to be identified to yield multi-factor models with good explanatory power. In this paper, we propose a novel subset selection method which makes use of the properties in the frequency domain environment. The proposed system ensures key patterns in the target variable be sought and suitable factors be selected based on frequency peaks in common. It can perform well even when the number of factors is significantly greater than the sample size. Moreover, a very important feature of the proposed system is the capability of handling factors with different timeframes, which is lacking in existing methods. We demonstrate the system via several examples with dataset from finance, economic, road traffic and air pollution.

Original languageEnglish
Pages (from-to)195-220
Number of pages26
JournalInternational Journal of Information Technology and Decision Making
Issue number1
Publication statusPublished - 1 Jan 2020


  • Subset selection
  • big data analytics
  • frequency analysis
  • multi-factor model

ASJC Scopus subject areas

  • Computer Science (miscellaneous)


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