  In todays global economy, accuracy in predicting macro-economic parameters
such as the foreign the exchange rate or at least estimating the trend
correctly is of key importance for any future investment. In recent times, the
use of computational intelligence-based techniques for forecasting
macroeconomic variables has been proven highly successful. This paper tries to
come up with a multivariate time series approach to forecast the exchange rate
(USD/INR) while parallelly comparing the performance of three multivariate
prediction modelling techniques: Vector Auto Regression (a Traditional
Econometric Technique), Support Vector Machine (a Contemporary Machine Learning
Technique), and Recurrent Neural Networks (a Contemporary Deep Learning
Technique). We have used monthly historical data for several macroeconomic
variables from April 1994 to December 2018 for USA and India to predict USD-INR
Foreign Exchange Rate. The results clearly depict that contemporary techniques
of SVM and RNN (Long Short-Term Memory) outperform the widely used traditional
method of Auto Regression. The RNN model with Long Short-Term Memory (LSTM)
provides the maximum accuracy (97.83%) followed by SVM Model (97.17%) and VAR
Model (96.31%). At last, we present a brief analysis of the correlation and
interdependencies of the variables used for forecasting.
