Hybrid systems using residual modeling for sea surface temperature forecasting

Domingos S. de O. Santos Júnior,Paulo S. G. de Mattos Neto,George D. C. Cavalcanti,Eraylson G. Silva

doi:10.1038/s41598-021-04238-z

Domingos S. de O. Santos Júnior, Paulo S. G. de Mattos Neto + Show 2 more

Open Access

https://doi.org/10.1038/s41598-021-04238-z

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Journal: Scientific Reports	Publication Date: Jan 11, 2022
Citations: 14	License type: open-access

Affiliation: Universidade Federal de Pernambuco

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 performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by 82.26%, 98.93%, and 65.03% compared to its respective single model. The HS employing the LSTM improved 92.15%, 98.69%, and 32.41% concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions.

Highlights

This experimental analysis is of crucial importance because of the adoption of hybrid systems: (i) it generally leads to more accurate results than single models in complex time series m odeling[16,17]; (ii) it is an efficient way of dealing with the problem of model selection with little extra e ffort[19]; and (iii) it is an effective manner to correct biased and/or misspecified forecasters[22,23]
It is important to highlight that the hybrid systems based on the long short-term memory (LSTM) model are more costly regarding computational effort than the ones based on support vector regression (SVR)
We evaluated two types of hybrid systems intending to improve the performance of single Machine Learning (ML) models in the task of Sea Surface Temperature (SST) forecast

Summary

Introduction

In SST forecasting, the works proposed in the literature commonly use a single method to model the time series under a nalysis[24] These approaches employ mainly linear statistical models or nonlinear ML models for this task[3,4,5,24,25,26,27]. To fulfill this gap, we perform an empirical evaluation of hybrid systems that use error series modeling in the context of SST time series forecasting.

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Conclusion