*(Thirty-sixth in a series)*

The forecasting methods we have discussed since the start of the *Forecast Friday* series have been quantitative. Formal quantitative models are often quite useful for predicting the near future, as the recent past often indicates expected results for the future. However, things change over time. While predictive models might be useful in forecasting the number of visits to your Web site next month, they may be less relevant to predicting your company’s social media patterns five or 10 years from now. Technology is likely to change dramatically during that time. Hence, more qualitative, or judgmental, forecasts are often required. Thus begins the next section of our series: Judgmental Methods in Forecasting.

Yet even with short-run forecasting, human judgment should be a part of the forecasts. A time series model can’t explain why a pattern is happening; it can only make predictions based on the patterns in the series it has “learned.” It cannot take into account the current environment in which those numbers came about, or information some experts in the field have about events likely to occur. Hence, forecasts by models should never be the “be-all, end-all.”

Essentially, there are two types of judgmental forecasting: subject matter expertise, which we will discuss in next week’s post, and judgmental extrapolation, which is today’s topic. *Judgmental extrapolation* – also known as *bold freehand extrapolation* is the crudest form of judgmental forecasting, and there’s really no expertise required to do it. Judgmental extrapolation is simply looking at the graph of a time series and making projections based upon visual inspection. That’s all there is to it; no understanding of the physical process behind the time series is required.

The advantage of judgmental extrapolation (the only one I could find, anyway) is its efficiency: it doesn’t require a lot of time, effort, understanding of the series, or money. But that’s efficiency, *not accuracy*! Sometimes when time and money are short, judgmental extrapolation is sometimes the only way to go. But if you have a time series already, you might get better results just plugging them into Excel and using its exponential smoothing or regression tools – and even that is relatively time and cost efficient.

Unfortunately, there’s no definitive findings from the published literature on the accuracy of judgmental extrapolation. I tend to be among its skeptics. Perhaps the strongest finding I’ve seen for the accuracy of judgmental forecasts (and it’s not really an argument in favor!) is that, when shown graphs of forecasts, individuals can adjust them in ways that improve the forecasts, *but only if the forecasts themselves are far from optimal!* That was the finding of T. R. Willemain, in a 1991 article in the *International Journal of Forecasting*.

So why do I mention judgmental extrapolation? As I said before, sometimes you need to make decisions quickly and without resources or adequate information. What’s more, judgmental extrapolation’s value – though not proven – has also not been disproven. Until its value is disproven, judgmental extrapolation should be considered another tool in the forecasting arsenal.

**Next Forecast Friday Topic: Expert Judgment**

Today we talked about forecasts relying upon non-expert judgment. Next week, we’ll talk about judgmental forecasts that are based on the opinion of subject matter experts.

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