Forecasting real exchange rate (REER) using artificial intelligence and time series models

Forecasting is an attractive topic in every field of study because no one knows the exact nature of the underlying phenomena, but it can be guessed using mathematical functions. As the world progresses towards technology and betterment, algorithms are updated to understand the nature of ongoing phenomena. Machine learning (ML) algorithms are an updated phenomenon used in every task aspect. Real exchange rate data is assumed to be one of the significant components of the business market, which plays a pivotal role in learning market trends. In this work, machine learning models, i.e., the Multi-layer perceptron model (MLP), Extreme learning machine (ELM) model and classical time series models are used, Autoregressive integrated moving average (ARIMA) and Exponential Smoothing (ES) model to model and predict the real exchange rate data set (REER). The data under consideration is from January 2019 to June 2022 and comprises 864 observations. This study split the data set into training and testing and applied all stated models. This study selects a model that meets the Key Performance Indicators (KPI) criteria. This model was selected as the best candidate model to predict the behaviour of the real exchange rate data set.

Automated summary

Forecasting is a popular topic in various disciplines due to the uncertainty of underlying phenomena, which can be estimated using mathematical functions. As technology advances, algorithms are updated to capture the ongoing phenomena. Machine learning algorithms, such as MLP, ELM, ARIMA, and ES models, are utilized to model and predict the real exchange rate data set. The study split the data into training and testing, and the model that best meets the KPI criteria is selected for predicting the behavior of the real exchange rate data set.