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In this paper, machine learning models for Forex prediction, evaluating traditional ensemble methods (Random Forest, XGBoost, LightGBM) against specialized time series models (Prophet, Arima, LSTM) across multiple currency pairs are compared. Performance assessment uses both statistical metrics (RMSE, MAE, directional accuracy) and trading measures (Sharpe ratio, maximum drawdown) across different market conditions. It is shown that ensemble methods excel with rich feature sets while time series models better capture temporal patterns. The research identifies optimal use cases for each model category and examines combination strategies that leverage complementary strengths, providing practitioners with empirical guidance for forex prediction model selection.
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