ARIMA Method in Predicting the Rupiah Exchange Rate:

The Effect of Daily and Monthly Data Frequency on the Accuracy of Short-Term Predictions

Abstract

The movement of the Rupiah exchange rate against the United States Dollar (USD) is one of the important economic indicators influenced by various external and internal factors. In time series analysis, the ARIMA (Autoregressive Integrated Moving Average) model is often used to predict the exchange rate. This study explores the impact of differences in data frequency, namely daily and monthly, on the accuracy of ARIMA predictions on the Rupiah exchange rate against the USD. Using historical exchange rate data from Bank Indonesia, this study found that monthly data produces more accurate predictions than daily data based on a comparison of the forecast value with the actual exchange rate on that day. This finding suggests that high volatility in daily data reduces ARIMA's ability to capture short-term patterns, while monthly data provides a more stable pattern for medium-term predictions. This study provides insights for economists, researchers, and practitioners in determining the optimal data frequency for currency exchange rate predictions.