Gold Price Forecast Based on the Least Square Support Vector Machine
Keywords:
Least Square Support Vector Machine, Gold Price, Forecasting, Parameter, trainingAbstract
Statistical models are used to forecast the price of gold. Utilize time-series based forecasting to ascertain how the past has an impact on the future. Forecasting is the process of creating theories about likely future occurrences, and forecasting models are capable of foreseeing such occurrences. Due to the worth of gold, systems for forecasting its price have garnered a lot of attention in the scientific and industrial realms. This study will forecast gold prices from 1 January 2021 to 12 may 2023 using a LSSVM model. Predictions of the price of gold are made using the proposed hybrid, Least square support vector machine (LSSVM) model. The analysis relies on the commonly used metrics of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) for assessing the performance of time-series forecasting. Performance evaluations using real data obtained from the MarketWatch gold prices show that the proposed LSSVM model outperforms other traditional statistical methods. The solution to problems involving time series forecasting has been greatly aided by the proposed method, which is also a very precise tool.
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