Cloud　services　have　rapidly　grown　in　cloud　data　centers　(CDCs).　Accurate　workload　prediction　benefits　CDCs　since　appropriate　resource　provisioning　can　be　performed　for　their　providers　to　ensure　the　full　satisfaction　of　service-level　agreement　(SLA)　requirements　fromusers.　Yet　these　providers　face　some　challenging　issues　in　accurate　workload　prediction,　i.e.,　how　to　achieve　high　accuracy　and　fast　learning　of　prediction　models.　Consistent　efforts　have　been　made　to　address　them.　This　letter　proposes　an　innovative　integrated　forecasting　method　that　combines　stochastic　configuration　networks　with　Savitzky-Golay　smoothing　filter　and　wavelet　decomposition　to　forecast　workload　at　the　succeeding　time　slot.　We　first　smooth　the　workload　via　a　Savitzky-Golay　filter.　Then,　we　adopt　wavelet　decomposition　to　decompose　smoothed　outcome　into　multiple　components.　Supported　by　stochastic　configuration　networks,　an　integrated　model　is　established,　which　can　well　describe　statistical　features　both　of　detail　and　trend　components.　Extensive　experimental　outcomes　have　explicated　that　our　approach　realizes　better　prediction　results　and　quicker　training　than　those　of　representative　prediction　approaches.