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Study on Currency Exchange Rates Using LSTM( Long Short-Term Memory) Neural Networks and Statistical Analysis

November 29, 2022


This paper focuses on the studies conducted on the changes in the exchange rate behavior of selected currencies such as USD, EURO, KRW, and PESO. The importance of the exchange rate is immeasurable since the movement in currency exchange affects the trade and direction of all money between countries. The studies done for this paper were used to observe the changes in this exchange rate on a global basis. Data collection provided a clear overview of the distribution of these studied rates. The exchange rate of each currency was predetermined, and the information was used to create visual histograms and other graphs for prediction. The data from the line graph provided a specific accounting of the movement of the rates over a period of time. The histograms used the information to decide which rate was standard and it was found that the rates fluctuated regularly but always peaked at a specific time. The predictions show that there are no specific patterns and the rates peaked sharply while the modeling was being conducted.  

The average range of these fluctuation patterns continued to change regularly until 2021. The predictive model was found using LSTM where it was determined that this data could be used to make significant advancements in the exchange rate volatility on economic growth. It was found that several of the studies determined that the high volatility of the exchange rate had a positive effect on international trade and economic growth. Those who support this theory feel that increased flexibility combined with these volatile exchange rates allow countries to stimulate economic growth. They also determined that as the volatility decreases, the result could present a global financial crisis. When observing these contradictions, it was clear that the impact of exchange rate volatility affects international trade. The bottom line is that economic growth continues to be a significant financial issue. 

Keywords: Exploratory Data Analysis(EDA)Exchange Rates, LSTM( Long Short-Term Memory), Neural Networks and Statistical Analysis


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