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　prediction　performance　for　resource　usage　and　workload　sequences.　Besides,　there　is　much　noise　in　original　time　series　of　resources　and　workloads.　If　these　time　series　are　not　de-noised　by　smoothing　algorithms,　the　prediction　results　can　fail　to　meet　the　providers＇　requirements.　To　do　so,　this　work　proposes　a　hybrid　prediction　model　named　VAMBiG　that　integrates　Variational　mode　decomposition,　an　Adaptive　Savitzky-Golay　(SG)　filter,　a　Multi-head　attention　mechanism,　Bidirectional　and　Grid　versions　of　Long　and　Short　Term　Memory　(LSTM)　networks.　VAMBiG　adopts　a　signal　decomposition　method　named　variational　mode　decomposition　to　decompose　complex　and　non-linear　original　time　series　into　low-frequency　intrinsic　mode　functions.　Then,　it　adopts　an　adaptive　SG　filter　as　a　data　pre-processing　tool　to　eliminate　noise　and　extreme　points　in　such　functions.　Afterwards,　it　adopts　bidirectional　and　grid　LSTM　networks　to　capture　bidirectional　features　and　dimension　ones,　respectively.　Finally,　it　adopts　a　multi-head　attention　mechanism　to　explore　importance　of　different　data　dimensions.　VAMBiG　aims　to　predict　resource　usage　and　workloads　in　highly　variable　traces　in　clouds.　Extensive　experimental　results　demonstrate　that　it　achieves　higher-accuracy　prediction　than　several　advanced　prediction　approaches　with　datasets　from　Google　and　Alibaba　cluster　traces.　IEEE