What-if Wednesday: Forecast Simulation – Changing The Beta Parameter in a Forecasting Method

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.

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.

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