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- W4377833688 abstract "International businesses are using foreign currencies as part of their activities. To optimize the cost of foreign exchange, the prediction of the exchange rate can be crucial. For time series forecasting, moving average techniques should be adopted. In this study, the exchange rate of EUR/USD is mainly focused on. The data under consideration are the Forex transaction rates which include open, high, low, and close. Furthermore, four financial factors are the dollar index, interest rate, inflation rate, and real growth of domestic products which are also included as inputs. Four models were employed: linear regression, multilayer perceptron, recurrent neural networks, and general regression neuron networks for predicting the Forex rate. Mean squared error is used for comparing forecasting performance. The results show that applying a sliding-window and moving average techniques improves the efficiency of the prediction." @default.
- W4377833688 created "2023-05-24" @default.
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- W4377833688 date "2023-01-01" @default.
- W4377833688 modified "2023-09-26" @default.
- W4377833688 title "Sliding-Window Technique for Enhancing Prediction of Forex Rates" @default.
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- W4377833688 doi "https://doi.org/10.1007/978-3-031-30474-3_18" @default.
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