Improved of forecasting sea surface temperature based on hybrid ARIMA and support vector machines models

Nawi, Wan Imanul Aisyah Wan Mohamad and Lola, Muhamad Safiih and Zakariya, Razak and Zainuddin, Nurul Hila and Abd Hamid, Abd Aziz K. and Aruchunan, Elayaraja and Nazzrol, Nor Shairah Azurah (2021) Improved of forecasting sea surface temperature based on hybrid ARIMA and support vector machines models. Malaysian Journal of Fundamental and Applied Sciences, 17 (5). pp. 609-620. ISSN 2289-5981,

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Forecasting is a very effortful task owing to its features which simultaneously contain linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) model has been one the most widely utilized linear model in time series forecasting. Unfortunately, the ARIMA model cannot effortlessly handle nonlinear patterns alone. Thus, Support Vector Machine (SVM) model is introduced to solve nonlinear behavior in the datasets with high variance and uncertainty. The purposes of this study are twofold. First, to propose a hybrid ARIMA models using SVM. Secondly, to test the effectiveness of the proposed hybrid model using sea surface temperature (SST) data. Our investigation is based on two well-known real datasets, i.e., SST (modis) and in-situ SST (hycom). Statistical measurement such as MAE, MAPE, MSE, and RMSE are carried out to investigate the efficacy of the proposed models as compared to the previous ARIMA and SVMs models. The empirical results reveal that the proposed models produce lesser MAE, MAPE, MSE, and RMSE values in comparison to the single ARIMA and SVMs models. In additional, ARIMA-SVM are much better than compared to the existing models since the forecasting values are closer to the actual value. Therefore, we conclude that the presented models can be used to generate superior predicting values in time series forecasting with a way higher forecast precision.

Item Type: Article
Uncontrolled Keywords: Accuracy; Forecasting; Hybrid; Sea Surface Temperature; SVMs
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Science > Institute of Mathematical Sciences
Depositing User: Ms Zaharah Ramly
Date Deposited: 25 May 2022 08:28
Last Modified: 25 May 2022 08:28

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