Time-series forecasting predicts future values based on historically observed sequential data points — stock prices, website traffic, energy consumption, or weather patterns. AI approaches include traditional statistical methods (ARIMA), classical ML (random forests on engineered features), and deep learning (LSTMs, transformers, temporal fusion transformers). A Moroccan retailer could forecast demand for each product in each store to optimize inventory, reducing both stockouts and overstock waste.