Predicting and Forecasting Metal Stock Prices Using Machine Learning

Machine Learning algorithms analyze past market data to forecast future trends in metal stock prices. Precise estimation of metal stock values is essential for industry stakeholders, policy makers, and investors. Correctly predicting market trends can be challenging as there are large number of underlying factors that affect shifts in the price of stocks. Autoregressive Integrated Moving Average (ARIMA) and Random Forest algorithm are two techniques used to predict prices of different metal genres. Non-linear correlations between features and underlying temporal patterns in the data can both be captured using ARIMA and Random Forest. A preliminary process is taken to guarantee the quality of the historical stock data, which includes transaction volume, opening and closing prices, and technical indicators. Random Forest model use both historical data and supplementary features, ARIMA models are trained on time-series data to extract temporal patterns. The models are able to learn from important aspects influencing metal equity prices in addition to the intrinsic time-series characteristics. To improve forecast accuracy, this includes outside variables such as geopolitical events, economic indicators, and market structures. The above technique offers a thorough grip of the market dynamics affecting metal genres prices by combining technical and fundamental analysis. The initiative intends to give investors with the skills and knowledge required to make good decisions in the ever-changing metal stock market environment by combining cutting-edge machine learning algorithms with domain expertise and outside data sources. These models aim to capture the tangled relationships between market variables and historical price data to detect patterns and trends that direct future price fluctuations.