SARIMA-GARCH and LSTM Performance for Broiler Meat Price Forecasting: A Case Study in West Sumatra - Wijaya, Joshua Bryan; Dzulkharifah, Indah; Putra, Varel Geo Syah; Abdurahman, Harits; Mualifah, Laily Nissa Atul; Pangesti, Windi (International Journal of Advances in Data and Information Systems, 2026)
The price of broiler chicken meat in West Sumatra is characterized by strong seasonality and high volatility. As a primary source of animal protein and a key contributor to regional inflation, accurate forecasting of these price fluctuations is essential for economic stability and policymaking. This study aims to compare the forecasting performance of the SARIMA-GARCH hybrid model against the Long Short-Term Memory (LSTM) model. The dataset consists of 1,198 daily observations spanning from 15 July 2022 to 24 October 2025, sourced from the National Food Agency (Badan Pangan Nasional). The results demonstrate that the SARIMA-GARCH model outperforms the LSTM model in terms of point forecast accuracy, as evidenced by lower prediction error metrics. Furthermore, the hybrid model successfully satisfies the statistical diagnostic criteria for volatility modeling by effectively resolving ARCH effects, ensuring the statistical validity of the residuals. While the LSTM model produces smoother long-term forecasts, the SARIMA-GARCH model effectively captures daily price fluctuations and indicates a modest upward trend over the next 28 days. These findings suggest that SARIMA-GARCH provides a more realistic depiction of short-term price movements for this specific regional market, offering a localized framework for stakeholders in West Sumatra to anticipate future market changes and maintain price stability.