Exponential smoothing is a statistical technique used for forecasting time series data. It involves using weighted averages of past observations, with the weights decaying exponentially. This means that more recent observations have a greater influence on the forecast than older observations. Exponential smoothing is particularly useful for data with trends and seasonal patterns. There are several types of exponential smoothing methods, including: 1. **Simple Exponential Smoothing**: This method is used for time series data without trends or seasonal patterns.
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With a fixed gain , exponential smoothing recursively combines the newest observation with the preceding estimate. Iteration assigns geometric weights to older observations, together with a diminishing weight on the initial estimate. The steady-state local-level Kalman gain gives a probabilistic interpretation of this recursion.