Exponential smoothing is a forecasting technique used to predict future values based on past data. It assigns exponentially decreasing weights to older observations, meaning more recent data has a greater influence on the forecast. This method is particularly useful for time series data, where trends and patterns can change over time. There are different types of exponential smoothing, including simple exponential smoothing, Holt’s linear method, and Holt-Winters seasonal method. Each variation is designed to handle specific data characteristics, such as trends or seasonality, making exponential smoothing a versatile tool for analysts and businesses in various industries.