Forecasting using Holt-Winters

We will now explore the methods of building the forecasting model using the Holt-Winters method with the HoltWinters function, which belongs to the forecasting package. This function computes the Holt-Winters filtering for a given time series data, and the unknown parameters are determined by minimizing the squared prediction error.

Apart from passing the time series data as an input, the other important parameters that need to be passed to this function are the alpha, beta, and gamma, as follows:

• alpha: The parameter of the Holt-Winters filters

• beta: This is used for the trend component; when set to false, the function will do exponential smoothening

• gamma: This is used for the seasonal component; when set to false, the nonseasonal component is fitted

Let's execute the following code where the trend component is set to FALSE, and hence, exponential smoothening will be performed on the dataset. We have set the gamma value as 0.5:

h_model=HoltWinters(ts, beta...