Exchange Rate Prediction Using Time Series Approach

Many countries suffer from high inflation due to global issues, and when a developed country has severe inflation, its currency rate falls. The study focuses on predicting the exchange rate with the modeling technique of time series using the Akaike Information Criterion (AIC). The seasonal ARIMA model of (0,1,0)(2,1,1)12 has been selected for prediction purposes based on the ACF, PACF, and seasonality components. Based on this chosen model, exchange rates have been predicted and compared with the historical dataset to validate the model. The model can be helpful for an investor who wants to invest in the currency/forex market.