Exponential Smoothing
Exponential smoothing is a time series forecasting method that uses weighted averages of past observations, with weights decreasing exponentially as observations get older. It is widely used for smoothing data and making short-term predictions in fields like economics, finance, and inventory management. The method is simple, computationally efficient, and adapts well to trends and seasonality through various extensions.
Developers should learn exponential smoothing when building forecasting models for applications such as demand prediction, stock price analysis, or resource planning, as it provides a lightweight alternative to complex models like ARIMA. It is particularly useful in real-time systems or environments with limited computational resources, where quick, adaptive forecasts are needed without heavy statistical overhead.