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Modelling South African Gold Sales Using SARIMA, GARCH and Neural Networks | Synapse

April 15, 2026Open Access

Modelling South African Gold Sales Using SARIMA, GARCH and Neural Networks

Key Points

The aim is to evaluate the forecasting accuracy of various models for South African gold sales data.
Utilized SARIMA, GARCH, GRNN, and ANN-ELM models to forecast gold sales.
Conducted comparative analysis across different forecasting models.
Implemented traditional and machine learning approaches for enhanced forecasting.
Identified distinct forecasting performances among the models used.
Machine learning models demonstrated advantages over traditional methods in certain conditions.

Abstract

This study investigated the forecasting performance of the South African gold sales series using the seasonal autoregressive integrated moving average (SARIMA), generalised autoregressive conditionally heteroscedastic (GARCH), general regression neural network (GRNN) and artificial neural network (ANN)-based extreme learning machine (ELM). This study employed traditional methods and a recently developed ML method for single hidden-layer feed-forward neural networks (SLFNs). The findings revealed that

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Cite This Study

Tsoku et al. (Mon,) studied this question.

synapsesocial.com/papers/69df2b04e4eeef8a2a6affbb https://doi.org/https://doi.org/10.3390/math14081289