What is Exponential Smoothing and its Types? – Expert Guide

When examining data from particular time periods, exponential smoothing prioritizes the more recent data while downplaying the significance of the earlier data. With the use of this technique, patterns and trends are easily seen in "smoothed data," or data that had the noise eliminated.

Three Different Approaches to Exponential Smoothing

Simple, double, and triple exponential smoothing are the three different forms of exponential smoothing models.

For time-series data without a seasonal pattern or trend, a single or simple exponential smoothing is utilized. It needs a solitary smoothing parameter that regulates the amount that historical observations (coefficient value lies between 0 and 1) influence the model. Smaller numbers in this technique specify that majority of the history is considered when making predictions, while values near 1 indicate that the model gives little regard to previous observations.

For time-series data with a trend but no seasonality, double exponential smoothing is utilized. Adding a second smoothing factor to the single exponential smoothing method improves upon it by enabling both linear and exponential trends by reducing the influence of the trend's significant decay.

For time-series data with a trend and seasonal pattern, triple exponential smoothing is sometimes referred to as Holt-Winters exponential smoothing. This strategy serves as a foundation for the previous two strategies, with a third parameter determining the seasonal component's impact.

Each method basically refines the parameters from the previous methods to account for new variables and produce forecasts with more accuracy. 

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