Abstract
Currency exchange prediction refers to the advance knowledge of the currency exchange rate. This can be done by studying the behavior of the historical data and applying some mathematical, statistical, or machine learning approaches. A large number of techniques are applied to predict the currency conversion rate. Nowadays, machine learning approaches are more popular due to their ability to produce more accurate results and predicted values. Artificial Neural Network (ANN) is a very popular technique in machine learning. However, the performance of ANN may also be improved by hybridizing ANN with some optimization techniques. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) could be two popular choices for this. This paper implements two hybrid models of machine learning namely Artificial Neural Network optimized with Genetic Algorithm (ANN-GA) and another model of Artificial Neural Network optimized with Particle Swarm Optimization (ANN-PSO). The data set used for the experiments is the currency exchange data of Indian Rupee and US dollar. The results show that the ANN-PSO model performs better than ANN-GA model for prediction of currency exchange.
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Muskaan, Sarangi, P.K., Singh, S., Nayak, S.R., Bhoi, A.K. (2022). Prediction of Currency Exchange Rate: Performance Analysis Using ANN-GA and ANN-PSO. In: Mallick, P.K., Bhoi, A.K., Barsocchi, P., de Albuquerque, V.H.C. (eds) Cognitive Informatics and Soft Computing. Lecture Notes in Networks and Systems, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-16-8763-1_29
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