Exponential Smoothing is a forecasting technique that assigns exponentially decreasing weights to past observations. The most recent data points are given more weight, while older observations lose their significance more quickly. This method is well-suited for time series with no clear trend or seasonal patterns, though it can be adapted to handle both. The basic form of exponential smoothing is called Single Exponential Smoothing, but there are more advanced versions, such as Double Exponential Smoothing (which accounts for trends) and Triple Exponential Smoothing (which incorporates seasonality). Exponential smoothing is favored in forecasting because it is easy to implement, requires minimal data processing, and performs well when trends or seasonal components are not too strong.