Comparative Analysis of ML Models for Currency Exchange Rate Prediction

The primary aim of this research is to improve the prediction of the exchange rate between the United States Dollar (USD) and the Indian Rupee (INR), which is an area that has received little attention in the field of financial forecasting. In contrast to widespread methodologies that consolidate results over several currency pair, this study specifically concentrates on the USD to INR pair, recognising the distinctive economic and political dynamics between the United States and India. This study aims to do a comparative analysis of four various machine learning models, namely RNN, ARIMA, LSTM, and Random Forest, in order to determine the best effective tool for predicting exchange rates in a certain context. This research employs a holistic methodology, including a wide range of variables like trade balances, interest rates, and geopolitical events, so providing a multifaceted forecasting approach. The growing economic interdependence between the United States and India highlights the practical importance of precise predictions for many stakeholders, such as traders, investors, and politicians. Furthermore, the study examines the concept of dynamic model updating, a novel attribute that augments flexibility within the ever-changing financial industry. The primary objective of this work is to address a significant need in current academic research by developing a reliable and practical instrument for accurately forecasting the exchange rate between the United States Dollar (USD) and the Indian Rupee (INR). This research is notable for its specific concentration, use of comparative methods, and its potential to make substantial advancements in both academic and practical domains pertaining to currency exchange rate forecasting.