What do you need to know about Single Exponential smoothing?
Single Exponential Smoothing, SES for short, also called Simple Exponential Smoothing, is a time series forecasting method for univariate data without a trend or seasonality. It requires a single parameter, called alpha (a), also called the smoothing factor or smoothing coefficient.
When do you use Holt Winters exponential smoothing?
This method is used for forecasting the time series when the data has both linear trend and seasonal pattern. This method is also called Holt-Winters exponential smoothing. The triple exponential smoothing formulas are given by: Here, s t = smoothed statistic, it is the simple weighted average of current observation x t.
What are the Gamma factors in exponential smoothing?
In addition to the alpha and beta smoothing factors, a new parameter is added called gamma ( g) that controls the influence on the seasonal component. As with the trend, the seasonality may be modeled as either an additive or multiplicative process for a linear or exponential change in the seasonality.
How does smoothing work in simpleexpsmoothing class?
First, an instance of the SimpleExpSmoothing class must be instantiated and passed the training data. The fit () function is then called providing the fit configuration, specifically the alpha value called smoothing_level. If this is not provided or set to None, the model will automatically optimize the value.
What is the value of β in Holt’s exponential smoothing?
Holt’s Linear Exponential Smoothing. When this done in Statgraphics, the estimates turn out to be α =0.3048 and β =0.008. The very small value of β means that the model assumes very little change in the trend from one period to the next, so basically this model is trying to estimate a long-term trend.
How to use double and triple exponential smoothing in Python?
Double and Triple Exponential Smoothing Single, Double and Triple Exponential Smoothing can be implemented in Python using the ExponentialSmoothing Statsmodels class. First, an instance of the ExponentialSmoothing class must be instantiated, specifying both the training data and some configuration for the model.