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　network　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.