Exponential Smoothing is a forecasting technique used to predict future values based on past observations. It applies decreasing weights to older data, meaning that more recent data points have a greater influence on the forecast. This method is particularly useful for time series data that exhibit trends or seasonal patterns. There are several types of Exponential Smoothing, including Simple, Holt’s Linear, and Holt-Winters methods. Each type caters to different data characteristics, allowing for more accurate predictions. By adjusting the smoothing parameters, users can fine-tune the model to better fit their specific data sets and forecasting needs.