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This paper examines whether ML models can beat the random walk benchmark in forecasting USD/CAD exchange rates, finding that only linear regression statistically outperforms the naive model, with SHAP analysis showing short-term lags dominate predictions.
Proposes G²C-MT, a graph-guided context selection framework for document-level machine translation that models structured discourse dependencies via a lightweight discourse graph and depth-biased random walk, outperforming baselines on multiple LLMs.