Home⇛International Journal of Multidisciplinary: Applied Business and Education Research⇛vol. 6 no. 11 (2025)

Cross-Attention Multimodal Transformer for Calibrated Binary Time-Series Forecasting of Rural Public Services

Daryl John C. Ragadio

Discipline: Education

 

Abstract:

Good governance, evidence-based planning, and sustainable rural de-velopment all rely on correct rural public service performance predic-tions. This study presents a Cross-Attention Multimodal Transformer developed for binary time-series classification of service conditions in the areas of agriculture, health, and environment at the level of the local government unit (LGU). Using bidirectional cross-attention lay-ers, the model mixes several temporal signals so that healthcare and agriculture-environment streams can interact with one another. Us-ing weighted uncertainty and calibration-awareness in a loss function helps to guarantee that the confidence scores are properly calibrated. With AUCs of 83.00% (agriculture), 79.40% (environment), and 63.90% (healthcare), which is lower, experimental results on a rural public service dataset indicate great discriminative and calibration performance. With 61.50%, 23.10%, and 18.90% respectively, the Brier scores suggest that the forecasts for health care and the envi-ronment are well calibrated. These findings suggest that cross-atten-tion multimodal transformers may be quite useful in producing pre-cise binary predictions of rural service results. At the LGU level, this would enable data-driven decision-making support.



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