Significant wave height forecasting is a key problem in ocean data analytics. This problem is relevant in several maritime operations, such as managing the passage of vessels or estimating the energy production from waves. In this work, we focus on the prediction of extreme values of significant wave height that can cause coastal disasters. This task is framed as an exceedance probability forecasting problem. Accordingly, we aim to estimate the probability that the significant wave height will exceed a predefined critical threshold. This problem is usually solved using a probabilistic binary classification model. Instead, we propose a novel approach based on a forecasting model. A probabilistic binary forecast streamlines information for decision-making, and point forecasts can provide additional insights into the data dynamics. The proposed method works by converting point forecasts into exceedance probability estimates using the cumulative distribution function. We carried out experiments using data from a buoy placed on the coast of Halifax, Canada. The results suggest that the proposed methodology is better than state-of-the-art approaches for exceedance probability forecasting.
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