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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 V ariational mode decomposition, an A daptive Savitzky-Golay (SG) filter, a M ulti-head attention mechanism, Bi directional and G rid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and …