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Exploring the role of wavelet decomposition order in deep learning-based network-wide traffic prediction

Mohammad Javad Hassanzada Iuliia Yamnenko Constantinos Antoniou

交通运输工程学报（英文版）2026，Vol.13Issue(1)：148-167,20.

交通运输工程学报（英文版）2026，Vol.13Issue(1)：148-167,20.DOI:10.1016/j.jtte.2024.08.003

Exploring the role of wavelet decomposition order in deep learning-based network-wide traffic prediction

Mohammad Javad Hassanzada ¹Iuliia Yamnenko ²Constantinos Antoniou¹

作者信息

1. Department of Mobility Systems Engineering,Technical University of Munich,Munich 80333,Germany
2. Department of Mobility Systems Engineering,Technical University of Munich,Munich 80333,Germany||Department of Electronic Devices and Systems,National Technical University of Ukraine"Igor Sikorsky Kyiv Polytechnic Institute",Kyiv 03056,Ukraine
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摘要

关键词

Network traffic forecast/Deep neural network/Wavelet processing order/MLP/Haar wavelet/SARIMA

Key words

Network traffic forecast/Deep neural network/Wavelet processing order/MLP/Haar wavelet/SARIMA

引用本文复制引用

Mohammad Javad Hassanzada,Iuliia Yamnenko,Constantinos Antoniou..Exploring the role of wavelet decomposition order in deep learning-based network-wide traffic prediction[J].交通运输工程学报（英文版）,2026,13(1):148-167,20.

基金项目

This research was funded by the Institute for Advanced Study at Technical University of Munich(TUM-IAS)and the Philipp Schwartz Initiative offered by the Alexander von Humboldt Foundation,Germany. （TUM-IAS）

交通运输工程学报（英文版）

ISSN：2095-7564

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