Comparison of ARIMA, LSTM, and Ensemble Averaging Models for Short-Term and Long- Term Forecasting of Non-Stationary Time Series Data

Pratiwi, Windy Ayu (IPB University)
Sumertajaya, I Made (IPB University)
Notodiputro, Khairil Anwar (IPB University)

Inferensi
Vol 8, No 3 (2025)

This study aims to forecast the highest weekly selling rate of the Indonesian Rupiah (IDR) against the US Dollar (USD) and identify the most accurate model among ARIMA, LSTM, and Ensemble Averaging. The evaluation results indicate that ARIMA achieves an accuracy of 97.75%, demonstrating strong performance in short-term forecasting, while LSTM achieves 99.98% accuracy, excelling in capturing complex and dynamic patterns in long-term predictions. The Ensemble Averaging approach attains the highest accuracy of 99.99%, proving to be the optimal solution by combining ARIMA’s stability with LSTM’s adaptability, resulting in more precise and stable predictions. The findings of this study highlight that the ensemble approach is more effective than individual models, as it balances accuracy and prediction stability across various forecasting scenarios. This method serves as a reliable tool for addressing market volatility and contributes significantly to the advancement of financial and economic forecasting techniques that are more adaptive and accurate.