Exponential Smoothing

Exponential smoothing is a time series forecasting method that uses a weighted average of past observations to make predictions. The weights decrease exponentially as the observations get older, giving more importance to recent observations. The method is particularly useful for data with a trend or seasonal pattern, as it can capture these patterns and make accurate predictions. Exponential smoothing can be applied to both univariate and multivariate time series data, and there are several variations of the method, including simple exponential smoothing, Holt's linear exponential smoothing, and Holt-Winters' seasonal exponential smoothing.
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