Network　traffic　prediction　is　fundamental　for　ensuring　network　reliability,　security,　and　optimal　management.　Despite　the　insights　from　conventional　prediction　methodologies,　the　evolving　network　dynamics　call　for　enhanced　prediction　techniques.　To　address　this　challenge,　this　paper　proposes　a　novel　method　using　the　Complete　Ensemble　Empirical　Mode　Decomposition　with　Adaptive　Noise　(CEEMDAN).　The　input　time　series　is　decomposed　into　multiple　Intrinsic　Mode　Functions　(IMFs)　and　a　residual　component,　providing　deep　insight　into　the　network　traffic　dynamics.　The　WaveNet　model　is　then　applied　to　forecast　individual　IMFs,　and　their　integration　produces　the　overall　network　traffic　prediction.　Notably,　our　approach　surpasses　conventional　methods　in　prediction　accuracy,　empowering　network　administrators　to　further　optimize　network　stability　and　security　measures.　©　2023　ACM.