Time series forecasting is the task of predicting future values based on historical sequential data ordered by time—fundamental to fields from finance and demand planning to weather prediction and infrastructure monitoring. Unlike cross-sectional analysis, forecasting must respect temporal dependencies, trends, and seasonality, making it uniquely challenging yet powerful. The landscape has evolved from classical statistical models like ARIMA to sophisticated deep learning architectures and hybrid approaches, each suited to different data patterns and forecasting horizons. A key insight: stationarity matters—most traditional models assume constant statistical properties over time, and transformations like differencing or decomposition often unlock otherwise intractable patterns.