Comparative Analysis of RNN, LSTM, and GRU Methods for Predicting the Value of the S&P GSCI Nickel Stock Index

Ragada Rahmansyah; Anik Vega Vitianingsih; Rusdi Hamidan; Anastasia Lidya Maukar; Yoyon Arie Budi Suprio

doi:10.24014/ijaidm.v8i2.36129

Comparative Analysis of RNN, LSTM, and GRU Methods for Predicting the Value of the S&P GSCI Nickel Stock Index

Ragada Rahmansyah, Anik Vega Vitianingsih, Rusdi Hamidan, Anastasia Lidya Maukar, Yoyon Arie Budi Suprio

Abstract

The development of information technology has opened up new opportunities in stock market forecasting, especially in nickel commodities, which are increasingly strategic in the global energy transition. This study uses a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a Gated Recurrent Unit (GRU) to forecast the movement of the S&P GSCI Nickel stock index value. Yahoo Finance time series data for the years 2018–2024 are used in the dataset. The study's findings are used to evaluate each model's capacity to forecast changes in nickel stock prices. The RNN model is used in this study because it can work with sequential information, while LSTM works with three memory gates (input, forget, output), and GRU works with 2 gates, namely update and reset. Mean Absolute Percentage Error (MAPE) presents the results of open and closed variable forecasting errors with the lowest average for the RNN model of 2.08%, the LSTM model of 2.505%, and the GRU model of 1.505%. This study is expected to contribute to investor decision-making and the identification of the most accurate forecasting model for the nickel stock index

Keywords

Forecasting; GRU; LSTM; Nickel; RNN

References

A. Arfan and L. ETP, “Perbandingan Algoritma Long Short-Term Memory dengan SVR Pada Prediksi Harga Saham di Indonesia,” Petir, vol. 13, no. 1, pp. 33–43, 2020, doi: 10.33322/petir.v13i1.858.

F. Pakaja, A. Naba, and Purwanto, “Peramalan Penjualan Mobil Menggunakan Jaringan Syaraf Tiruan dan Certainty Factor,” Eeccis, vol. 6, no. 1, pp. 23–28, 2012, doi: 10.21776/jeeccis.v6i1.162.

S. S. Naibaho and S. R. Simangunsong, Indonesia Gudang Nikel, Indonesia Memimpin Transisi Energi, vol. 2, no. 28. 2022. doi: 10.33088/jspi.v2i1.

D. N. Tsirwiyati, “Kebijakan Larangan Ekspor Nikel Indonesia,” J. Huk. Respublica Fak. Huk. Univ. Lancang Kuning, vol. Xi, no. 231, pp. 1–12, 2023, doi: 10.31849/respublica.v22i2.13468.

R. Ciptaswara, “IMPLEMENTASI HILIRISASI MINERAL DAN BATU BARA DALAM RANGKA MEWUJUDKAN KEDAULATAN ENERGI DAN DAYA SAING INDUSTRI NASIONAL,” vol. 34 no 2, pp. 521–558, 2022, doi: 10.22146/mh.v34i2.3490.

R. D. Martono, “Analisis Pengaruh Harga Komoditas Dunia Terhadap Pergerakan Indeks Harga Saham Gabungan (IHSG), Indeks Lq 45 , Dan Jakarta Islamic Index (JII) Di Bursa Efek Indonesia (BEI),” no. 106081002483, pp. 1–106, 2019.

G. Budiprasetyo, M. Hani’ah, and D. Z. Aflah, “Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM),” J. Nas. Teknol. dan Sist. Inf., vol. 8, no. 3, pp. 164–172, 2023, doi: 10.25077/teknosi.v8i3.2022.164-172.

A. Y. Pramudia, “Implementasi Metode Autoregressive Integrated Moving Average (ARIMA) pada Aplikasi Peramalan Harga Saham Berbasis Website,” J. Ilm. Komputasi, vol. 22, no. 1, pp. 105–112, 2023, doi: 10.32409/jikstik.22.1.3335.

N. Prissy, M. Al Haris, and P. R. Arum, “Peramalan Nilai Ekspor Migas Di Indonesia Menggunakan Model Long Short Term Memory Dan Gated Recurrent Unit Dengan Optimasi Nesterov Adam,” J Stat., vol. 16, no. 1, pp. 12–26, 2022, doi: 10.36456/jstat.vol16.no1.a6121.

T. P. Shella, “Hybrid Autoregressive Integrated Moving Average (ARIMA) – Gated Recurrent Unit (GRU) Dalam Peramalan Harga Sawit Pada Pt. Sawit Sumbermas Sarana Di Kalimantan Tengah,” 2023.

M. N. Alim, “Pemodelan Time Series Data Saham LQ45 dengan Algoritma LSTM, RNN, dan Arima,” Pros. Semin. Nas. Mat., vol. 6, pp. 694–701, 2023,

I. A. Saputra, A. V. Vitianingsih, Y. Kristyawan, A. L. Maukar, and J. F. Rusdi, “Forecasting Model of Export and Import Value of Oil and Gas Using Gated Recurrent Unit Method,” Teknika, vol. 13, no. 2, pp. 239–243, 2024, doi: 10.34148/teknika.v13i2.861.

K. A. Rijal, A. V. Vitianingsih, Y. Kristyawan, A. L. Maukar, and S. F. A. Wati, “Forecasting Model of Indonesia’s Oil & Gas and Non-Oil & Gas Export Value using Var and LSTM Methods,” J. Teknol. dan Manaj. Inform., vol. 10, no. 1, pp. 59–69, 2024, doi: 10.26905/jtmi.v10i1.13127.

M. Joseph, Modern Time Series Forecasting with Python. 2022.

M. V. E. . Makridakis S., Wheelwright S.S., “Forecasting Methods and Applications ),” 2008.

A. C. M. and S. Guido, Introduction to Machine Learning with Python by, no. 0. 2016.

J. M. Czum, “Dive Into Deep Learning,” J. Am. Coll. Radiol., vol. 17, no. 5, pp. 637–638, 2020, doi: 10.1016/j.jacr.2020.02.005.

K. Sofien, Deep Learning for Finance. 2024.

J. Brownlee, “Long Short-Term Memory Networks With Python,” Mach. Learn. Mastery With Python, vol. 1, no. 1, p. 228, 2017.

P. Goodwin, J. K. Ord, L.-E. Öller, J. A. Sniezek, and M. Leonard, Principles of Forecasting: A Handbook for Researchers and Practitioners, vol. 18, no. 3. 2002. doi: 10.1016/s0169-2070(02)00034-1.

DOI: http://dx.doi.org/10.24014/ijaidm.v8i2.36129

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