Prediction of IDR-USD Exchange Rate using the Cheng Fuzzy Time Series Method with Particle Swarm Optimization

Abstract

Currently, much research on machine learning about prediction has been carried out. For example, to predict the exchange rate of the rupiah against the United States currency, namely the United States Dollar (USD). The continuing trend of USD depreciation has attracted many researchers to explore currency trading, especially in establishing an efficient method for predicting fluctuating exchange rates. The rapid development of time series prediction methods has resulted in many methods that can predict data according to needs. In this study, we apply the Fuzzy Time Series Cheng method with Particle Swarm Optimization (PSO) to predict the IDR exchange rate against USD. The data used in this research is sourced from Bank Indonesia in the form of time series data on the selling and buying exchange rate. The FTS Cheng method forecasts the IDR exchange rate against USD. In contrast, the PSO algorithm optimizes the interval parameter to increase the forecasting accuracy. Based on the implementation and the results of the tests, the results show that using the PSO algorithm can produce the best optimization interval parameters and increase the accuracy value. From the results of 10 trials with training data, testing data, and different iterations, it was obtained that the MAPE test for predicting the rupiah exchange rate against the US dollar using FTS Cheng with 60% training data and 40% testing data resulted in the lowest MAPE of 0.610145%. Furthermore, 70% of the training and 30% of the testing data resulted in the lowest MAPE of 0.313388%. Then the FTS Cheng and PSO testing with 60% training data and 40% testing data, and an iteration value of 200 resulted in the lowest MAPE of 0.394707%. Furthermore, 70% of training data and 30% of testing data and an iteration value of 90 resulted in the lowest MAPE of 0.263666%.