Time Series Analysis in Forecasting Nickel Prices Using the ARIMA and Double Exponential Smoothing Methods
Abstract
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
References
REFERENCE
Abror Gustiansyah, M., Rizki, A., Apriyanti, B., Maulidia, K., Julio Roger Roa, R., Al Hadi, O., Ghoni Trisno Hidayatulloh, N., Andriyani Lestari Ningsih, W., Putri Ratnasari, A., & Angraini, Y. (2023). Aplikasi Model ARIMA dalam Peramalan Data Harga Emas Dunia Tahun 2010-2022. Jurnal Statistika Dan Aplikasinya, 7(1). https://doi.org/10.21009/JSA.07108
Agus, D. D. (2026, March). Harga Komoditas Nikel untuk Kontrak 3 Bulan ke Depan Turun Menuju Level US$17.165 /Ton (Rabu, 18 Maret 2026). Databoks Katadata. https://databoks.katadata.co.id/pasar/statistik/fb73cc493c80e11/harga-komoditas-nikel-kontrak-3-bulan-hari-ini
Asmaradana, A. A., Widodo, E., & Artikel, R. (2023). Penerapan Metode Peramalan Double Exponential Smoothing Pada Indeks Harga Konsumen Kota Yogyakarta P-Issn E-Issn. In Emerging Statistics and Data Science Journal (Vol. 1, Number 1). https://doi.org/10.20885/esds.vol1.iss.1.art4
Carista, N., Mahadesyawardani, A., Dwiyanto, A. S., & Pusporani, E. (2025a). Forecasting Nickel Prices in Indonesia Using ARIMA, SVR, and Hybrid ARIMA-SVR Approach. Jurnal Matematika, Statistika Dan Komputasi, 21(3), 627–645. https://doi.org/10.20956/j.v21i3.42558
Carista, N., Mahadesyawardani, A., Dwiyanto, A. S., & Pusporani, E. (2025b). Forecasting Nickel Prices in Indonesia Using ARIMA, SVR, and Hybrid ARIMA-SVR Approach. Jurnal Matematika, Statistika Dan Komputasi, 21(3), 627–645. https://doi.org/10.20956/j.v21i3.42558
Parmanto, V. E. P., Achmadi, S., & Mahmudi, A. (2025). Penerapan Metode Double Exponential Smoothing Untuk Peramalan Penjualan Sembako. JATI (Jurnal Mahasiswa Teknik Informatika), 9(6), 10961-10967. https://doi.org/10.36040/jati.v9i6.15477
Gempati, A., Fradani, F. A. R., Ibrahim, R. M., Astuti, T. K., Prasetyo, Y. R., & Devi, L. Y. (2025). Peramalan Data Ihsg 2021-2025 Di Indonesia Dengan Time Series Modeling Autoregressive Integrated Moving Average (Arima). Jurnal Ilmiah Ekonomi Dan Manajemen, 3(5), 225-234. https://doi.org/10.61722/jiem.v3i5.4650
Habsari, H. D. P., Purnamasari, I., & Yuniarti, D. (2020). Forecasting Uses Double Exponential Smoothing Method and Forecasting Verification Uses Tracking Signal Control Chart (Case Study: IHK Data Of East Kalimantan Province). Barekeng, 14(1), 013–022. https://doi.org/10.30598/barekengvol14iss1pp013-022
Harahap, M. G., & Novitasari, E. D. (2022). Geomorfologi Dan Karakteristik Nikel Laterit Di Desa Baingkete Distrik Makbon Kabupaten Sorong Provinsi Papua Barat. INTAN Jurnal Penelitian Tambang, 5(2), 27-34. https://doi.org/10.56139/intan.v5i2.147
Jayanti, L. D., Lestari, R., Suparno, F. A. D., & Fachruzzaki, F. (2025). Prediksi Harga Emas Tahun 2024-2025 dengan Metode Autoregressive Integrated Moving Average (ARIMA) pada Aplikasi RStudio. MARAS : Jurnal Penelitian Multidisiplin, 3(4), 1275–1289. https://doi.org/10.60126/maras.v3i4.1253
Lubis, R. M. F., Situmorang, Z., & Rosnelly, R. (2021). Autoregressive Integrated Moving Average (ARIMA-Box Jenkins) Pada Peramalan Komoditas Cabai Merah di Indonesia. Jurnal Media Informatika Budidarma, 5(2), 485. https://doi.org/10.30865/mib.v5i2.2927
Melina, Sukono, Napitupulu, H., Mohamed, N., Chrisnanto, Y. H., Hadiana, A. I. D., Kusumaningtyas, V. A., & Nabilla, U. (2024). Comparative Analysis Of Time Series Forecasting Models Using Arima And Neural Network Autoregression Methods. Barekeng, 18(4), 2563–2576. https://doi.org/10.30598/barekengvol18iss4pp2563-2576
Mufida, F. A., Eltivia, N., & Riwajanti, I. (2024). Time Series Forecasting of Nickel Sales in Nickel Mining Companies Listed on Indonesia Stock Exchange (IDX). 6(2). https://doi.org/10.32877/ef.v6i2
Nahuway, Y., Ekowati, C. K., & Madu, A. (2025). Analisis data time series menggunakan metode ARIMA untuk meramalkan persentase lulusan tepat waktu di FKIP Undana. NOTASI: Jurnal Pendidikan Matematika, 3(1), 46–55. https://doi.org/10.70115/notasi.v3i1.285
Nuha, H. H. (2024). Mean Squared Error (MSE) dan Penggunaannya. JAPATUM) Japatum.Matradipti.Org, 3(4), 2025. https://doi.org/10.59328/
Peychinov, T., Karaivanova, A., & Mecheva, T. (2025). Predicting Traffic Load Data: ARIMA and SARIMA Comparison. Engineering Proceedings, 100(1). https://doi.org/10.3390/engproc2025100029
Pirmanto, D. (2024). Double Exponential Smoothing Forecasting Food Crop Yields Using Geographic Information Systems. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 13(2), 179–185. https://doi.org/10.32736/sisfokom.v13i2.2069
R.M., M. C. (2024). Nickel Price Projection Using Multivariate Regression Method and Investment Feasibility Analysis of Nickel Mining in Indonesia with Sensitivity Analysis and Monte Carlo. International Journal of Current Science Research and Review, 07(03). https://doi.org/10.47191/ijcsrr/V7-i3-34
Shakeel, H. M., Iram, S., Hill, R., Athar Farid, H. M., Sheikh-Akbari, A., & Saleem, F. (2025). A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings. Buildings, 15(8). https://doi.org/10.3390/buildings15081275
Shumway, R. H. ; S. D. S. (2017). Time Series Analysis and Its Applications: With R Examples (4th ed.). Springer. https://doi.org/10.1007/978-3-319-52452-8
Yuliawanti, F. D., Novitasari, D. C. R., Widodo, N., Hamid, A., & Utami, W. D. (2021). Application of Autoregressive Integrated Moving Average (ARIMA) Method for Sunspot Number Prediction. Barekeng, 15(3), 555–564. https://doi.org/10.30598/barekengvol15iss3pp555-564
Widarti, W., Darina, N., Chasanah, S. L., & Setiawan, E. (2024). A Penerapan Metode Double Moving Average dan Double Exponential Smoothing pada Peramalan Nilai Impor Barang Konsumsi Tahun 2017-2022. MATHunesa: Jurnal Ilmiah Matematika, 12(1), 30-37. https://doi.org/10.26740/mathunesa.v12n1.p30-37
