Efficient Time Series Forecasting with MSPA for Smart Government Big Data Platforms

Smart government platforms face challenges in handling heterogeneous data and missing information in knowledge graphs. This paper presents MSPA, an efficient time series prediction model combining multi-scale pooling and attention mechanisms. The model converts 1D time series to 2D format, applies multi-scale pooling (2×2, 3×3, 4×4 kernels) for feature compression, and uses single-head attention to capture global dependencies while reducing computational complexity from O(H×L²) to O(0.42L²). MSPA effectively predicts missing data dynamics to improve knowledge graph integrity. Experiments demonstrate superior prediction accuracy and sixfold efficiency improvement over existing methods, providing a novel solution for intelligent government decision-making and urban governance.