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- W4312912643 abstract "The market of foreign exchange is one of the largest markets worldwide. However, predicting the price of exchange currency pairs is a very difficult problem due to the fact that exchange rate time series demonstrate a highly non-linear and non-stationary behavior, being affected by a series of parameters which are difficult to model efficiently. This study attempts to compare five machine learning and neural network classifiers: Logistic Regression model, Support Vector Classifier, Gaussian Naive Bayes, Random Forest and Multi-layer Perceptron. The most highly correlated features are evaluated and compared for predicting the day ahead trend of the Euro-United States Dollar (EUR-USD) currency pair. Results indicate that model selection is not as significant as the combination of the most important features for the accuracy of the prediction." @default.
- W4312912643 created "2023-01-05" @default.
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- W4312912643 date "2022-07-18" @default.
- W4312912643 modified "2023-09-28" @default.
- W4312912643 title "Comparison of Machine Learning Classifiers for Exchange Rate Trend Forecasting" @default.
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- W4312912643 doi "https://doi.org/10.1109/iisa56318.2022.9904380" @default.
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