What if scenario forecast simulation: what is the effect of changing the beta parameter in Holt-Winters method?
In the previous blog, we experimented with Alpha – the intercept parameter of Holt-Winters method – to see how the forecast gets affected as the weight changes from 0.05 to 0.8.
This week we are going to run similar tests with Beta, the trend parameter. We will use the same historical data set for our analysis. As we discussed in the last post about Alpha, Beta can also take values ranging from 0 to 1; and the larger the value of Beta the more weightage is on the recent historical trend.
What if scenario forecast simulation Analysis Using The Beta Parameter
For our analysis, we will keep the alpha value at 0.05 and will vary the Beta value from 0.05 to 0.8 and evaluate how the forecast changes. You can see the numbers at the end of the post.
The first chart below shows historically observed values from the last three years and the following graphs represent forecasts using a Beta value of 0.05, 0.1, 0.2, 0.4 and 0.8.
We observe that as the Beta value changes from 0.05 to 0.1 to 0.2, there is minimal impact on the forecast. The trend continues to follow a general downward trend that exists from the last 3 years. But as we make the parameter large and put more weightage on the recent trend – 40 and 80 percent – we see the forecast picking up based on the trend from the last 5-6 months.[Related: Using What-if Scenarios To Create A Dynamic S&OP Paradigm ]
One can argue that when Beta is very high, 80%, the forecast should show a downward trend since the last months’ (Nov’16) actuals are less as compared to the previous month (Oct’16). But from the above charts, we see that it takes more than just 2 months to impact the trend, even when the parameter is set to be as high as 80%. Moreover, low values for November as compared to October have as much to do with seasonality as they have to do with the trend.
So, if there is a business case where a company has introduced a couple of new products and is seeing a growth in sales in the last 6 months, they can consider increasing the trend parameter so that the forecast follows the increasing trend.
In our next What-if Wednesday post, we will analyze how the seasonal parameter, Gamma, impacts the forecast.[Download: ERP and Planning Facts and Fallacies ]
Want to join in our What-if-Wednesday posts? Add a comment below or send a tweet to @Arkieva with #WhatifWednesday, or email email@example.com to suggest a topic, scenario or simulation that you would like us to discuss.