Forecasting Nickel Prices in Indonesia Using ARIMA, SVR, and Hybrid ARIMA-SVR Approach

Nashwa Carista; Arinda Mahadesyawardani; Adelia Sukma Dwiyanto; Elly Pusporani

doi:10.20956/j.v21i3.42558

Authors

Nashwa Carista Airlangga University
Arinda Mahadesyawardani Airlangga University
Adelia Sukma Dwiyanto Airlangga University
Elly Pusporani Airlangga University

DOI:

https://doi.org/10.20956/j.v21i3.42558

Keywords:

Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Hybrid ARIMA-SVR, Forecasting, Nickel

Abstract

Nickel production plays a key role in reducing reliance on fossil fuels, supporting the 7th Sustainable Development Goals (SDGs) on clean and affordable energy. As the world's largest nickel ore producer, Indonesia significantly influences global market dynamics. This study evaluates the accuracy of ARIMA, SVR, and hybrid ARIMA-SVR models in forecasting Indonesia’s daily nickel futures prices for 2023 using historical data from official website investing.com. The results indicate that SVR outperforms the other models, achieving the lowest MAPE of 0.2532% with the Radial Basis Function (RBF) kernel and optimized parameters , and selected through grid search method which gives the minimum RMSE and MAE values as well. Accurate nickel price forecasting is essential for investors, mining companies, and policymakers to optimize production planning, manage risks, and enhance market stability. However, this study relies solely on historical price data, without considering external factors such as geopolitical events and market shocks, highlighting the need for future research incorporating broader economic indicators and alternative modeling approaches

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