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Subset Selection Using Frequency Decomposition with Applications

Author

Listed:
  • W. M. Tang

    (Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom Kowloon, Hong Kong, P. R. China)

  • K. F. C. Yiu

    (Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom Kowloon, Hong Kong, P. R. China)

  • H. Wong

    (Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom Kowloon, Hong Kong, P. R. China)

Abstract

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.

Suggested Citation

  • W. M. Tang & K. F. C. Yiu & H. Wong, 2020. "Subset Selection Using Frequency Decomposition with Applications," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 195-220, March.
    • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:01:n:s0219622019500500
    DOI: 10.1142/S0219622019500500
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    References listed on IDEAS

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    Cited by:

    1. Yuancheng Si, 2020. "Pivot Property in Weighted Least Regression Based on Single Repeated Observations," Annals of Data Science, Springer, vol. 7(2), pages 291-306, June.
    2. David A. Alilah & C. O. Ouma & E. O. Ombaka, 2023. "Efficiency of Domain Mean Estimators in the Presence of Non-response Using Two-Stage Sampling with Non-linear and Linear Cost Function," Annals of Data Science, Springer, vol. 10(2), pages 291-316, April.

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