GOLD PRICE PREDICTION USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

Abstract

Gold was first used as a standard means of exchange in 643 B.C when it was used to create coins. During this ear, wealth was then defined as a function of the amount of gold possessed by individuals or countries. The impact of gold on the economy of any nation has a direct correlation with the safety and security of most related investments in the economy. Whenever other investment instruments look risky or filled will a high level of uncertainty, gold almost automatically assumes the place of a good hedge. Information on the speculation and trading of this metal abounds. Investors are attracted to moving their funds to gold as guaranteed storage of wealth, while traders capitalize on the dynamism of the market to build capital. The ups and downs in the price of gold and other precious metals can be predicted with proven mathematical and artificial intelligent algorithms. The researchers conducted a study using a machine learning algorithm in the price prediction of gold over a 10year period. Autoregressive Integrated Moving Average (ARIMA) model was used in the experiment, while Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) evaluation metrics were used in the evaluation of the performance of the various ARIMA models. The results obtained in the study proved that ARIMA could achieve high prediction performance over the entire period of prediction. The best prediction outcome of 98.23% was obtained during the 52 weeks period.

Keywords

• Precious Metal
• Prediction
• Machine Learning
• Artificial Intelligence
• ARIMA

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