Network time series forecasting using spectral graph wavelet transform

Kyusoon Kim and Hee-Seok Oh

International Journal of Forecasting, 2024, vol. 40, issue 3, 971-984

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.

Keywords: Forecasting; Graph Fourier transform; Graph signals; Graph wavelet transform; Network time series (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:3:p:971-984

DOI: 10.1016/j.ijforecast.2023.08.006

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