I'm using Holt Winters to predict sales revenue from past performance. Seasonality and changing trends exist in the data.
One of the reasons chosen for Holt Winters is that it is fairly simple (implementable in excel) and explainable to non-statisticians. If other methodologies are more appropriate I’m happy to hear about them.
A key implementation detail is to choose coefficients $\alpha, \beta$, and $\gamma$. Are there “standard” values for these?
The other reason for this methodology is that it auto-updates. This will be useful as the series will change with market shifts. I was planning to adjust the “standard” value by eye, to try to mitigate likely changes. Is this a reasonable approach?
Best Answer
You should not "choose" coefficients, you should estimate them from the data. The values of $\alpha$, $\beta$ and $\gamma$ control how quickly the model components change over time. What values are appropriate depend on the data. You can estimate them by minimizing the MSE, for example. Even in Excel you can do that using Solver.