Very few studies delve into modeling the future movement of the Peso-Dollar exchange rate. Notwithstanding the critical role the FX rate played in the economy is enormous. The study attempts to develop a hybrid model of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) in forecasting the future of the Peso-Dollar exchange rate. Using the Bangko Sentral ng Pilipinas (BSP) FX rate from 2000-2020 to predict the January and February FX rate, the ARIMA-ANN hybrid was developed and compared with the result Holt-Winters Model, ARIMA, and ANN. The outcome demonstrates that the hybrid is a better model with an absolute difference of less than 1 percent, 0.21 percent, and 0.42 percent forecast for January and February 2021, respectively, compared with the actual FX rate. Furthermore, the three statistical measures, MAE, MSE, and RMSE, were used to compare the four methods` performance and demonstrate that the hybrid model has the lowest measurement of errors (0.341, 0.208, and 0.457). Therefore, it is possible to present a more accurate technique in forecasting the FX rate with hybrid modeling.
Keywords: Peso-Dollar Exchange Rate, ARIMA, ANNAras, S., & Kocakoç, İ. D. (2016). A new model selection strategy in time series forecasting with artificial neural networks: IHTS. Neurocomputing, 174, 974-987.
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