Real-time　and　precise　prediction　for　traffic　of　networks　is　critically　important　for　allocating　the　optimal　computing/network　resources　based　on　users＇　business　requirements,　analyzing　the　network　performance,　and　realizing　intelligent　congestion　control　and　high-accuracy　anomaly　detection.　The　dramatic　growth　of　users＇　applications　significantly　increases　the　volume,　uncertainty,　and　complexity　of　workload,　thereby　making　it　highly　challenging　to　precisely　predict　future　net-work　traffic.　Temporal　Convolutional　Networks　(TCNs)　and　Long　Short-Term　Memory　(LSTM)　can　be　effectively　used　to　analyze　and　predict　time　series.　This　work　designs　an　improved　prediction　approach　for　the　prediction　of　network　traffic,　which　combines　a　Savitzky-Golay　filter,　TCN,　and　LSTM,　called　ST-LSTM　for　short.　It　first　removes　the　noise　of　data　with　the　filter　of　Savitzky-Golay.　It　then　investigates　temporal　characteristics　of　data　by　using　TCN.　At　last,　it　investigates　the　long-term　dependency　in　the　time　series　by　using　LSTM.　Experimental　results　on　a　real-life　website　dataset　show　the　prediction　accuracy　of　ST-LSTM　is　higher　than　autoregressive　integrated　moving　average,　support　vector　regression,　eXtreme　Gradient　Boosting,　backpropagation,　TCN,　and　LSTM,　in　terms　of　several　commonly　used　performance　indicators.　©　2022　IEEE.