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Routine monitoring for harmful algal blooms (HABs) is generally undertaken at low temporal frequency (e.g., weekly to monthly) that is unsuitable for capturing highly dynamic variations in cyanobacteria abundance. Therefore, we developed a model incorporating reverse time attention with a decay mechanism (RETAIN-D) to forecast HABs with simultaneous improvements in temporal resolution, forecasting performance, and interpretability. The usefulness of RETAIN-D in forecasting HABs was illustrated by its application to two sites located in