A Comparative Study of Artificial Neural Network (ANN) and Support Vector Regression (SVR) on Forecasting: A Review

Forecasting outcomes of any system is essential for a better understanding and optimal management of the fluxes occurring in system operations. Machine Learning (ML) approaches can solve complex relationships among collected data that are hard to describe using forecasting models. This paper aims to give an overview of many described prediction methodologies that use Artificial Neural Networks (ANN) and Support Vector Regression (SVR) under the diversity of the dataset and understand the performance of each method. To improve the forecasting performance, the author proposed depending on some performance indicators, such as The Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination R2. Concludes that SVR generally outperforms ANN in forecasting of groundwater quality, drought indices, oil production, and illuminance prediction. The ANN shows better performance in certain scenarios, such as predicting wheat moisture content, solar energy, and monthly streamflow.

Machine learning, prediction, artificial neural network, support vector regression, forecasting.

10.52113/2/12.01.2025/132-142