TIFUSE: SELECTIVE AND STABLE TIME–FREQUENCY FUSION FOR CLOUD WORKLOAD FORECASTING

Cloud workloads combine recurring periodicities with abrupt shifts and transient disturbances, requiring models to capture global structures and local dynamics simultaneously. Existing time–frequency forecasters typically learn both domains exhaustively and fuse them directly, which can preserve redundant information and make cross-domain interactions less stable. We propose TiFuse, a selective and stable time–frequency framework for cloud workload forecasting. TiFuse constructs a compact spectral representation by retaining energy-dominant DCT components, and a complementary temporal representation by selecting representative multi-scale historical patches while preserving recent dynamics. Their interaction is modeled with relation-enhanced attention that couples sample-specific dependencies with a global relational prior under input-dependent sparse constraints. Experiments on five real-world cloud traces covering CPU, memory, and GPU workloads show that TiFuse achieves the lowest average MSE in 15 of 16 dataset–resource settings and the lowest average MAE in 13 of 16, while maintaining competitive computational efficiency.