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Foreign Exchange Rate Forecasting
Built a forecasting pipeline for the EUR/USD currency pair using statistical baselines and deep learning. Combined classical time series analysis with recurrent and convolutional networks to surface medium-term trading signals for a graduate analytics project.
Approach
- Engineered features from macroeconomic indicators and technical signals (momentum, volatility, moving averages) to enhance model sensitivity.
- Benchmarked ARIMA/SARIMA models before progressing to stacked RNN and CNN architectures for capturing both long- and short-term patterns.
- Tuned hyperparameters with rolling-window cross validation to align training with real-world deployment constraints.
- Created dashboards that visualized forecasts, confidence intervals, and error diagnostics for presentation to faculty.
Highlights
- Improved directional accuracy over statistical baselines and produced sharable notebooks to support scenario analysis.
- Delivered a narrative report translating complex model outputs into actionable insights for technical trading recommendations.
- Documented an extensible roadmap for incorporating exogenous features and live data feeds in future iterations.