Hybrid systems using residual modeling for sea surface temperature forecasting.

ABSTRACT: The sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performan

SUBMITTER: de Mattos Neto PSG 

PROVIDER: S-EPMC8752630 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature