Exchange Rate Forecasting in Money Market

Changes in currency exchange rates can impact a country's economy, making it important to carefully consider and anticipate foreign currency exchange rates in the money market. Therefore, accurately predicting FOREX fluctuations is a significant concern. Convolutional neural networks (CNN) and random forest regression have been suggested as prediction algorithms for foreign exchange. The efficiency of the approach is assessed by forecasting the worth of the New Zealand dollar against the US dollar (NZU/USD)for 2, 4, 6, 8, 10, 12, and 14 weeks into the future. The best weight for the suggested Model is calculated utilizing the adaptive learning rate method (ADAM) optimization approach. The new approach provides an additional precise way to estimate foreign exchange rates (Forex) based on the estimation criteria. The CNN model with the layer for random forest regression performs better than the other three models.