This survey explores how explainable AI (XAI) techniques—such as SHAP, LIME, and interpretable models like attention-based LSTMs and decision trees—are applied to financial time series forecasting tasks like stock price prediction, volatility analysis, and algorithmic trading. By enhancing transparency and trust, these XAI approaches help financial professionals better understand model decisions, align predictions with market behavior, and support more informed investment strategies