Performance Comparison of SARIMA and Prophet Models for Forecasting Indonesian Banking Stock Prices
DOI:
https://doi.org/10.51903/pixel.v19i1.3959Keywords:
SARIMA, Prophet, stock price forecasting, time series, Indonesian banking stocksAbstract
This study compared the performance of SARIMA and Prophet models in forecasting daily close prices of three major Indonesian banking stocks: BBCA, BBRI, and BMRI, using data from January 2020 to March 2026. Data were retrieved via the yfinance library, preprocessed, and split into 80% training and 20% testing sets. SARIMA modeling followed the Box-Jenkins procedure, while Prophet was configured with a Lag-1 regressor, weekly and monthly seasonality, Indonesian public holidays, and log transformation. Model performance was evaluated using MAPE, MSE, and Dstat metrics. Results showed that SARIMA outperformed Prophet in MAPE and MSE across all six stock-variable combinations, with MAPE values ranging from 1.3368% to 1.9386% for SARIMA and 1.5992% to 2.2300% for Prophet. However, Prophet demonstrated marginally higher Dstat values in several series. Both models achieved "Very Good" forecasting accuracy. A web-based forecasting system was also developed using Streamlit to make the models accessible to investors.
References
[1] Artana WW, Indrawan G, Yota K, Aryanto E. Prediction of Damage to Electronic Devices Based On Power Consumption Characters Using Artifician Neural Network. Jurnal Ilmu Komputer Indonesia (JIK) 2021;5.
[2] Wirakusuma KA, Ni Putu Novita Puspa Dewi, Kadek Yota Ernanda Aryanto. OPTIMASI MODEL PREDIKSI EXTREME GRADIENT BOOSTING DENGAN GENETIC ALGORITHM UNTUK PRODUKSI DAN PRODUKTIVITAS PADI. STORAGE: Jurnal Ilmiah Teknik Dan Ilmu Komputer 2025;4:378–92. https://doi.org/10.55123/storage.v4i4.6633.
[3] Pascima IBN, Putrama IM. Forecasting foreign exchange rate using a combination of linear regression and flower pollination algorithm. J. Phys. Conf. Ser., vol. 1810, IOP Publishing Ltd; 2021. https://doi.org/10.1088/1742-6596/1810/1/012021.
[4] Sudipa IGI, Riana R, Putra INTA, Yanti CP, Aristana MDW. Trend Forecasting of the Top 3 Indonesian Bank Stocks Using the ARIMA Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika 2023;8:1883–93. https://doi.org/10.33395/sinkron.v8i3.12773.
[5] Arunika K, Dewi LJE. PERBANDINGAN MODEL SARIMA, EXPONENTIAL SMOOTHING, DAN XGBOOST UNTUK PREDIKSI PENJUALAN SUPER STORE. Jurnal Informatika Dan Teknik Elektro Terapan 2025;13. https://doi.org/10.23960/jitet.v13i3S1.8173.
[6] Pascima IBN, Listartha IME. Perbandingan Metode Deep Learning pada Peramalan Data Penjualan. Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika (KARMAPATI) 2025;14:265–73. https://doi.org/10.23887/karmapati.v14i2.104103.
[7] Schaffer AL, Dobbins TA, Pearson S-A. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol 2021;21:58. https://doi.org/10.1186/s12874-021-01235-8.
[8] Ferdinand F V., Santoso TH, Saputra KVI. Performance Comparison Between Facebook Prophet and SARIMA on Indonesian Stock. 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE; 2023, p. 1–5. https://doi.org/10.1109/IEEM58616.2023.10406940.
[9] Suryani R, Indradewi IGAAD, Pascima IBN. IMPLEMENTASI METODE SARIMA DALAM MEMPREDIKSI JUMLAH PELANGGARAN LALU LINTAS DI KABUPATEN BULELENG. Jurnal Informatika Dan Teknik Elektro Terapan 2026;14. https://doi.org/10.23960/jitet.v14i1.8827.
[10] Taylor SJ, Letham B. Forecasting at Scale. Am Stat 2018;72:37–45. https://doi.org/10.1080/00031305.2017.1380080.
[11] Vishwakarma A, Singh A, Mahadik A, Pradhan R. Stock Price Prediction Using Sarima and Prophet Machine Learning Model. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) 2021;9.
[12] Kwarteng S, Andreevich P. Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting. Research & Development 2024;5:110–20. https://doi.org/10.11648/j.rd.20240504.13.i
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