Coefficient

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