Time Series Forecasting: Analysis of LSTM Neural Networks to Predict Exchange Rates of Currencies

The global financial and economic market is now made up of several structures that are powerful and complex. In the last few decades, a few techniques and theories have been implemented that have revolutionized the understanding of those systems to forecast financial markets based on time series analysis. However still, none has been shown to function successfully consistently. In this project, a special form of Neural Network Modeling called LSTM to forecast the foreign exchange rate of currencies. In several different forecasting applications, this method of modelling has become popular as it can be defined complex non-linear relationships between variables and the outcome it wishes to predict. In compare to the stock market, exchange rates tend to be more relevant due to the availability of macroeconomic data that can be used to train the network to learn the impact of particular variables on the rate to be predicted. The information was collected using Quandl, an economic and financial platform that offers quantitative indicators for a wide variety of countries. Model is compared with three different metrics by exponential moving average and an autoregressive integrated moving average. then compare and validate the ability of the model to reliably predict future values and compare which of the models predicted the most correctly.