Mobile　traffic　prediction　opens　a　promising　avenue　to　demand-aware　large-scale　resource　allocation　with　a　significant　improvement　in　the　spectral　efficiency.　Various　long-term　prediction　methods　have　been　proposed　in　the　literature.　However,　when　considering　the　stringent　requirement　of　the　real-time　and　efficient　radio　resource　allocation　for　future　wireless　communications,　developing　short-term　prediction　methods　with　high　prediction　accuracy　is　more　desirable.　In　this　paper,　we　exploit　spatiotemporal　correlations　among　the　mobile　traffic　data　and　propose　a　novel　machine　learning-based　short-term　prediction　method,　referred　to　as　spatiotemporal　Gaussian　Process　Kalman　filter　(ST-GPKL)　method,　which　includes　two　phases:　the　model　selection　and　inference.　The　function　of　the　model　selection　is　to　fine-tune　the　hyperparameters　of　the　designed　kernel　function,　while　that　of　the　inference　incorporates　the　Kalman　filter　to　predict　the　future　mobile　data　traffic.　Compared　with　the　conventional　methods,　the　proposed　one　can　significantly　improve　the　prediction　accuracy,　resulting　in　much　higher　efficiency　in　large-scale　resource　allocation.　©　2020　IEEE.