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Network time series forecasting using spectral graph wavelet transform
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AI
DOI：10.1016/j.ijforecast.2023.08.006.png)
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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. (c) 2023 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Forecasting
Graph Fourier transform
Graph signals
Graph wavelet transform
Network time series
期刊
IF:
7.1
论文数:
3.1K
被引数:
9.9K
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Graph Signal Processing: Overview, Challenges, and Applications图信号处理: 概述、挑战与应用.png)
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PROCEEDINGS OF THE IEEEIF25.9
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