Historical Data-Based Gold Price Prediction using Intelligent Algorithms

Abstract: Gold's price is always fluctuating, either rising or falling. Given that gold is a major element of the financial market, gold price prediction is an essential area of finance. Many machine-learning methods have been used in published studies to anticipate gold prices. Several classification techniques, including random forest, decision tree, logistic regression, and linear regression, are used in this work. This article's topic originates from study done to understand the worth of gold. There is currently a constant market for gold. The gold price trend shows that gold is one of the best investment strategies. It is, therefore, prudent to forecast the direction of the gold rate. Numerous statistical models can be used to forecast and model data. The price of gold is consistently shown to be nonlinear. Price prediction is key to sound financial and investing strategy. The price fluctuation of gold can be represented as an exponential curve. Convolutional neural networks are among the best tools for resolving nonlinearities in data, and RNNs are especially useful for time series forecasting and estimation. Using data from the World Gold Council, it is found that the suggested design is among the most effective financial forecasting techniques.

Keywords: Regression, linear regression, logistic regression, decision tree, random forest, Machine Learning and l Prediction. Cite: Dhanush N, Raghavendra R, "Historical Data-Based Gold Price Prediction using Intelligent Algorithms", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13302.