Komparasi Akurasi Model SARIMA, LSTM, dan GRU dalam Peramalan Indeks Harga Saham S&P 500
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1322Keywords:
GRU; LSTM; SARIMA; S&P 500 Index; Stock Price ForecastingAbstract
Movement of S&P 500 stock price index is non-linear and influenced by numerous macroeconomic factors, making closing price forecasting a critical challenge for investors and portfolio managers in investment decision-making. This study aims to determine whether deep learning models outperform classical statistical models in forecasting non-linear patterns in the daily closing price data of the S&P 500 index using a public dataset. The forecasting models employed are Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The three models were independently constructed and trained, then evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R-squared). Results show that GRU delivered the best performance, with an MAE of 87.04, RMSE of 100.72, MAPE of 2.94 percent, and R-squared of 0.836, followed by LSTM with an MAE of 105.89, RMSE of 132.13, MAPE of 3.55 percent, and R-squared of 0.718. SARIMA performed considerably worse, with an MAE of 418.77, RMSE of 610.20, MAPE of 13.72 percent, and a negative R-squared of -5.02, indicating its inability to capture the non-linear patterns and strong trends in the data. These findings confirm that deep learning models, particularly GRU, outperform classical statistical models in forecasting highly volatile stock indices, while also offering better computational efficiency than LSTM.
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Copyright (c) 2026 Imam Adiyana, Angga Kurniawan, Bella Okta Sari Miranda
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