Time Series Forecasting is a statistical modeling technique used to predict future values based on historical time-ordered data points. It is commonly applied in various domains, including finance, sales forecasting, weather prediction, and resource planning, to make informed decisions and plan for the future. Readers can explore how time series forecasting methods, such as ARIMA (AutoRegressive Integrated Moving Average), exponential smoothing, and deep learning models, enable organizations to forecast trends, identify patterns, and make accurate predictions from time-dependent data, improving decision-making and planning processes.