# Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction

arXiv: https://arxiv.org/abs/2206.09821 · HTML: https://mlsanity.com/paper/2206.09821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Evaluating time series forecasting models: An empirical study on performance estimation methods](https://mlsanity.com/paper/1905.11744/) (2019)
- [Significant Wave Height Prediction based on Wavelet Graph Neural Network](https://mlsanity.com/paper/2107.09483/) (2021)
- [Deep Learning for Energy Markets](https://mlsanity.com/paper/1808.05527/) (2018)
- [Model Compression for Dynamic Forecast Combination](https://mlsanity.com/paper/2104.01830/) (2021)

## Built on by (most cited first, 1 of 1)

- [Forecasting threshold exceedance of atmospheric variables at a specific location](https://mlsanity.com/paper/2605.31079/) (2026)

## Abstract (arXiv)

Significant wave height forecasting is a key problem in ocean data analytics. This task affects 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 or an ensemble of forecasts. Instead, we propose a novel approach based on point forecasting. Computing both type of forecasts (binary probabilities and point forecasts) can be useful for decision-makers. While a probabilistic binary forecast streamlines information for end-users concerning exceedance events, the point forecasts can provide additional insights into the upcoming future dynamics. The procedure of the proposed solution works by assuming that the point forecasts follow a distribution with the location parameter equal to that forecast. Then, we convert these point forecasts into exceedance probability estimates using the cumulative distribution function. We carried out experiments using data from a smart 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.