📅 28 August 2024
DOI: 10.35671/telematika.v17i2.2932
Comparison of Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in Forecasting Commodity Prices
Telematika
Universitas Amikom Purwokerto
📄 Abstract
In this study, we compare the performance of both hybrid and non-hybrid forecasting models, explicitly focusing on Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in predicting commodity prices within the volatile market of Central Java, Indonesia. The primary objective is to evaluate which hybrid and non-hybrid models provide the most accurate and reliable forecasts under various conditions. Analyzing daily price data from the SiHaTi platform, an official service provided by Bank Indonesia, the Hybrid ARIMA-LSTM model emerges as the most accurate, achieving a forecast accuracy of 92.5%, compared to the 78.3% and 84.7% accuracies of Linear Regression and ARIMA, respectively. These findings underline the potential advantages of combining machine learning with statistical methods to improve predictions in dynamic market conditions, providing invaluable insights for policymakers and market analysts. However, it should be noted that only one hybrid model was compared, and future research should explore multiple hybrid models to ensure a comprehensive evaluation of their effectiveness.
🔖 Keywords
#Forecasting Commodity Prices; ARIMA; Simple Exponential Smoothing; Hybrid ARIMA-LSTM; EWMA
ℹ️ Informasi Publikasi
Tanggal Publikasi
28 August 2024
Volume / Nomor / Tahun
Volume 17, Nomor 2, Tahun 2024
📝 HOW TO CITE
Mujiyanto, Mujiyanto; Universitas AMIKOM Yogyakarta; Nurindahsari, Susi; Politeknik Harapan Bersama; Nurul Izza, Rahmafatin; Politeknik Elektronika Negeri Surabaya; Universitas AMIKOM Yogyakarta, "Comparison of Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in Forecasting Commodity Prices," Telematika, vol. 17, no. 2, Aug. 2024.
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