Time Series Analysis and Prediction of Future Commodities Prices with SARIMA

Siyou Yao

2025

Abstract

Commodities are one of the most important investing targets in the world. Forecasting the prices of commodities precisely helps investors and corporations make reasonable transactions. This paper is based on the prices of Brent oil, wheat, and aluminum and predicts the future prices of commodities in the energy industry, the manufacturing industry, and the agricultural products industry. The results show that the Seasonal Autoregressive Integrated Moving Average (SARIMA) model has a relatively excellent ability to predict the future movements of the commodities prices. The prediction shows that all the commodities prices will experience a decline in the next 10 months. However, the predictions are not exactly the same as the actual movements. The fluctuation extent of the predictions is much smaller. Therefore, the SARIMA model can help investors establish a broad idea of the future trend of the commodities prices, but it cannot help investors do ultrashort-term trading. If the investors only focus on the trend during a longer period and ignore making profits with the short-term fluctuations, the SARIMA model is suitable for them. In the end, this paper suggests investors combine the fundamental analysis with the forecasting results generated by the SARIMA model to make trading decisions.

Paper Citation

in Harvard Style

Yao S. (2025). Time Series Analysis and Prediction of Future Commodities Prices with SARIMA. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 467-475. DOI: 10.5220/0013699500004670

in Bibtex Style

@conference{icdse25,
author={Siyou Yao},
title={Time Series Analysis and Prediction of Future Commodities Prices with SARIMA},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={467-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013699500004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Time Series Analysis and Prediction of Future Commodities Prices with SARIMA
SN - 978-989-758-765-8
AU - Yao S.
PY - 2025
SP - 467
EP - 475
DO - 10.5220/0013699500004670
PB - SciTePress