Non-linear autoregressive neural network with exogenous variable in forecasting USD/IDR exchange rate

Restu Arisanti, Mentari Dara Puspita

Abstract


In the history of economic development, the Rupiah exchange rate is considered one of the key factors in economic stability in Indonesia. Bank Indonesia’ policies play an important role in the Indonesia's economy. The purpose of this study is to explore the relationship between time effect and the presence of exogenous variable that affect the response variable in the non-linear model. The methodological approach taken in this study is Nonlinear Autoregressive Exogenous Neural Network Method (NARX NN). NARX NN is a powerful method for forecasting of time series data and dynamic control problems. NARX NN method extracts generic principles from the past values of the time series to predict its future values so that can be used to forecast the Rupiah exchange rate to the dollar based on the BI rate variable. The obvious finding to emerge from this study are: 1) Feed Forward Neural Network (FFNN) with rprop+ training algorithm, the best model is FFNN (12-3-1). The best FFNN model applied to all BI 7-day (Reverse) Repo Rate data has an Adjusted R2 of 98.8%; 2) Forecasting the USD/IDR exchange rate and its relation to the BI 7-day (Reverse) Repo Rate using the NARX NN series parallel model with the rptop+ training algorithm, obtained the best model NARX NN (13-4-1). The NARX NN model applied to all USD/IDR exchange rate data has an Adjusted R2 of 96.19%; 3) Based on forecasting results for the next 6 periods, the USD/IDR exchange rate tends to experience a downward trend, meaning that the Rupiah exchange rate strengthens, while the BI 7-day (Reverse) Repo Rate increases.

Full Text: PDF

Published: 2022-01-17

How to Cite this Article:

Restu Arisanti, Mentari Dara Puspita, Non-linear autoregressive neural network with exogenous variable in forecasting USD/IDR exchange rate, Commun. Math. Biol. Neurosci., 2022 (2022), Article ID 5

Copyright © 2022 Restu Arisanti, Mentari Dara Puspita. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

ISSN 2052-2541

Editorial Office: office@scik.org

 

Copyright ©2022 CMBN