[2603.25046] MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting

Computer Science > Artificial Intelligence arXiv:2603.25046 (cs) [Submitted on 26 Mar 2026] Title:MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting Authors:Huyen Ngoc Tran, Dung Trung Tran, Hong Nguyen, Xuan Vu Phan, Nam-Phong Nguyen View a PDF of the paper titled MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting, by Huyen Ngoc Tran and 4 other authors View PDF HTML (experimental) Abstract:Precipitation forecasting remains a persistent challenge in tropical regions like Vietnam, where complex topography and convective instability often limit the accuracy of Numerical Weather Prediction (NWP) models. While data-driven post-processing is widely used to mitigate these biases, most existing frameworks rely on point-wise objective functions, which suffer from the ``double penalty'' effect under minor temporal misalignments. In this work, we propose the Matrix Profile-guided Mixture of Experts (MP-MoE), a framework that integrates conventional intensity loss with a structural-aware Matrix Profile objective. By leveraging subsequence-level similarity rather than point-wise errors, the proposed loss facilitates more reliable expert selection and mitigates excessive penalization caused by phase shifts. We evaluate MP-MoE on rainfall datasets from two major river basins in Vietnam across multiple horizons, including 1-hour intensity and accumulated rainfall over 12, 24, and 48 hours. Experimental results demonstrate that MP-M...