Accurate　and　real-time　prediction　of　network　traffic　can　not　only　help　system　operators　allocate　resources　rationally　according　to　their　actual　business　needs　but　also　help　them　assess　the　performance　of　a　network　and　analyze　its　health　status.　In　recent　years,　neural　networks　have　been　proved　suitable　to　predict　time　series　data,　represented　by　the　model　of　a　long　short-term　memory　(LSTM)　neural　network　and　a　temporal　convolutional　network　(TCN).　This　article　proposes　a　novel　hybrid　prediction　method　named　SG　and　TCN-based　LSTM　(ST-LSTM)　for　such　network　traffic　prediction,　which　synergistically　combines　the　power　of　the　Savitzky-Golay　(SG)　filter,　the　TCN,　as　well　as　the　LSTM.　ST-LSTM　employs　a　three-phase　end-to-end　methodology　serving　time　series　prediction.　It　first　eliminates　noise　in　raw　data　using　the　SG　filter,　then　extracts　short-term　features　from　sequences　applying　the　TCN,　and　then　captures　the　long-term　dependence　in　the　data　exploiting　the　LSTM.　Experimental　results　over　real-world　datasets　demonstrate　that　the　proposed　ST-LSTM　outperforms　state-of-the-art　algorithms　in　terms　of　prediction　accuracy.