Currently,　cloud　computing　service　providers　face　big　challenges　in　predicting　large-scale　workload　and　resource　usage　time　series.　Due　to　the　difficulty　in　capturing　nonlinear　features,　traditional　forecasting　methods　usually　fail　to　achieve　high　performance　in　predicting　resource　usage　and　workload　sequences.　Much　noise　implicit　in　the　original　sequences　of　resources　and　workloads　is　another　reason　for　their　low　performance.　To　address　these　problems,　this　work　proposes　a　hybrid　prediction　model　named　SABG　that　integrates　an　adaptive　Savitzky-Golay　(SG)　filter,　Attention　mechanism,　Bidirectional　and　Grid　versions　of　Long　and　Short　Term　Memory　(LSTM)　networks.　SABG　adopts　an　adaptive　SG　filter　in　the　data　pre-processing　to　eliminate　noise　and　extreme　points　in　the　original　time　series.　It　uses　bidirectional　and　grid　LSTM　networks　to　capture　bidirectional　features　and　dimension　ones,　respectively.　Then,　it　utilizes　an　attention　mechanism　to　explore　importance　of　different　data　dimensions.　SABG　aims　to　predict　resource　usage　and　workloads　in　highly　variable　traces　in　cloud　computing　systems.　Extensive　experimental　results　demonstrate　that　SABG　achieves　higher-accuracy　prediction　than　several　benchmark　prediction　approaches　with　datasets　from　Google　cluster　traces.　　©　2022　IEEE.