Time Series Analysis for Commodity Price Forecasting

Abstract

Commodity price forecasting plays a crucial role in international trade, agriculture, investment, and economic decision-making. However, accurate prediction remains challenging due to market volatility, economic uncertainty, geopolitical events, climate change, and supply chain disruptions. This study proposes a comprehensive time series forecasting framework that integrates classical statistical models, including ARIMA, SARIMA, and Exponential Smoothing, with machine learning and deep learning techniques such as Random Forest Regression, Support Vector Regression, Gradient Boosting, and Long Short-Term Memory (LSTM). The framework incorporates data preprocessing, feature engineering, trend decomposition, stationarity testing, model optimization, and rolling window validation to improve forecasting performance. Model evaluation is conducted using MAE, RMSE, MAPE, SMAPE, and R² metrics. Experimental results demonstrate that hybrid forecasting models outperform conventional statistical approaches by effectively capturing nonlinear temporal patterns and improving prediction accuracy under volatile market conditions. The proposed framework offers a scalable, interpretable, and adaptable solution for commodity price forecasting, supporting strategic decision-making, risk management, inventory optimization, and investment planning across diverse commodity sectors.