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Network time series forecasting using spectral graph wavelet transform

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  • Kim, Kyusoon
  • Oh, Hee-Seok

Abstract

We propose a novel method for forecasting network time series that occur in graphs or networks. Our approach is based on a spectral graph wavelet transform (SGWT) that provides the localized behavior of graph signals around each node. The proposed method improves forecasting performance over other existing methods. In particular, the advantages of the proposed method stand out when signals observed on a graph are inhomogeneous or non-stationary. We demonstrate the strength of the proposed approach through real-world data analysis. This analysis uses two network time series datasets: the daily number of people getting on and off the Seoul Metropolitan Subway, and daily Covid-19 confirmed cases reported in South Korea. We further conduct a simulation study to evaluate the effectiveness of the proposed method.

Suggested Citation

  • Kim, Kyusoon & Oh, Hee-Seok, 2024. "Network time series forecasting using spectral graph wavelet transform," International Journal of Forecasting, Elsevier, vol. 40(3), pages 971-984.
    • Handle: RePEc:eee:intfor:v:40:y:2024:i:3:p:971-984
    DOI: 10.1016/j.ijforecast.2023.08.006
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Maarten Jansen & Guy P. Nason & B. W. Silverman, 2009. "Multiscale methods for data on graphs and irregular multidimensional situations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 97-125, January.
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