Optimizing Long Short-Term Memory to Predict Currency Rates

Abstract

As a travel destination, Saudi Arabia attracts individuals worldwide, including tourists, investors, and immigrant workers, for various purposes, including trip planning, investment decisions, and remittance transfers. Indonesia and Pakistan are the biggest countries that send Umrah and Hajj pilgrims. We need to predict currency rates in 3 pairs of currencies that are frequently used by travel agencies, Hajj and Umrah pilgrims, such as the Saudi Riyal (SAR) against the Pakistani Rupee, the SAR against the Indonesian Rupiah (IDR), and the United States Dollar (USD) against the IDR. This study utilizes Long Short-Term Memory (LSTM) models, the machine learning approach for predicting currency pairs exchange rates. Previous studies succeeded in predicting USD/IDR rates using the LSTM time series-machine learning approach, but the root mean square error (RMSE) value was the worst 271. The research aims to optimize the LSTM to predict the currency rate in the future using historical data obtained from investing.com. We use Python to predict the currency rate pairs, following an experimental investigation with adjustments to the batch size, epoch, and prediction days. The experimental results show that SAR/PKR has a smaller mean square error (MSE) of 0.94, RMSE of 0.97, and MAE of 0.61, while SAR/IDR and USD/IDR Excel with Models 2 and 1 have smaller MSEs of 317.79 and 6654.41, RMSEs of 17.82 and 81.57, and MAEs of 10.54 and 50.12, respectively.

Analisa Performa Algoritma Random Forest & Logistic Regression Dalam Sistem Credit Scoring

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“…Gambar 7. ROC Curve Random ForestDari hasil uji validasi model Random Forest memberikan hasil bahwa model Random Forest memiliki area cakupan di bawah kurva (AUC) yang cukup tinggi dan nilai AUC rata rata berada diangka 0.90 sehingga dari nilai tersebut, menunjukan bahwa model Random Forest memiliki kemampuan untuk melakukan prediksi dengan baik [10]. Gambar 8.…”