A Machine Learning Framework for Monthly Crude Oil Price Prediction with Cat Boost
DOI:
https://doi.org/10.63665/emkx0127Keywords:
Crude Oil Price Prediction, Machine Learning, CatBoost Regressor, Time Series Forecasting, Energy Economics, Predictive Analytics, Gradient Boosting, Historical Market Data, Feature Engineering, Rolling Statistics, Lag Features, Seasonal Analysis, RMSE, MAE, MAPE, Financial Forecasting, Oil Market Analysis, Data Science, Artificial Intelligence, Economic Prediction.Abstract
Crude oil is a globally significant energy resource whose price fluctuations have far-reaching economic and industrial impacts. Accurate forecasting of crude oil prices is crucial for strategic decision-making in sectors such as finance, energy, and transportation.
This project presents a machine learning-based approach to predict monthly crude oil prices using historical market data and engineered time-series features. The model is developed using the CatBoost Regressor, a high-performance gradient boosting algorithm known for its efficiency, accuracy, and ability to handle complex non-linear data.
The predictive features include: Lagged prices from previous months Rolling statistical indicators (mean and standard deviation) Temporal features such as month and year (encoded using sine and cosine transformations to preserve seasonality) Both percentage and absolute monthly price changes, The dataset spans over four decades (1983–2025), ensuring that the model captures long-term patterns and short-term fluctuations. Model performance is evaluated using RMSE, MAE, and MAPE metrics, demonstrating strong predictive accuracy and generalization capability.
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