Time Series Analysis in Forecasting Nickel Prices Using the ARIMA and Double Exponential Smoothing Methods - Vina Nurmadani; Rian Kurnia; Indah Suciati (Sciencestatistics: Journal of Statistics, Probability, and Its Application, 2026)
Nickel is one of the strategic commodities that plays an important role in global industries, particularly as the primary raw material in the production of stainless steel and electric vehicle batteries. The increasing demand for nickel, driven by technological advancements and the need for more environmentally friendly energy sources, causes nickel prices to fluctuate, making it necessary to employ methods capable of forecasting future price movements. This study aims to forecast nickel prices using the Autoregressive Integrated Moving Average (ARIMA) method and the Double Exponential Smoothing method, as well as to compare the performance of both methods. The data used in this research consist of secondary daily nickel price data with 62 observation periods. The research stages include data preprocessing, stationarity testing, modeling, and model evaluation using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results show that the best ARIMA model is ARIMA(2,1,1), which produces an MSE of 0.2797 and an RMSE of 0.5288. Meanwhile, the Double Exponential Smoothing method results in an MSE of 0.1299 and an RMSE of 0.3604. Based on these evaluation results, the Double Exponential Smoothing method demonstrates better performance than ARIMA in forecasting nickel prices in this study. This method is able to produce more accurate and stable predictions that follow the trend patterns of the data. Therefore, the Double Exponential Smoothing method is recommended as a more optimal approach for nickel price forecasting