High in the mountains of Colorado, Foresight editor-in-chief Len Tashman previews the new issue:
What proficiencies are essential for today’s business forecasters and planners? Sujit Singh offers a detailed and quite formidable list in Critical Skills for the Business Forecaster, our feature article in this 32nd issue of Foresight. While forecasters may not be required to understand all the various forecasting methods at an expert level, it is very important that they know the levers to pull to control the output from their forecasting solution. As Sujit notes, “Proper analysis by the forecaster will often show a clear separation between situations that should be forecast statistically and those that require manual input.”
A major contributor to Foresight in the past year, Sujit is also the subject of this issue’s Forecaster in the Field interview.
Forecasting Support Systems Editor Stavros Asimakopoulos then teams up with George Boretos and Constantinos Mourlas on an article that projects significant potential benefits to forecasters and planners from our many mobile devices. Forecasting “In the Pocket”: Mobile Devices Can Improve Collaboration points out that smartphones, tablets, and such offer a boon to forecasters by simplifying information flow and enabling more timely adaptations to new information. The onus is on vendors to design m-forecasting applications that optimize our mobile experience.
We continue our series of Forecasting Methods Tutorials with Geoff Allen’s primer on Regression Modeling for Business Forecasting. Regression is the principal statistical approach to incorporating business drivers into the forecasting model. Geoff takes us through the key attributes for understanding the structure of a regression model, the conditions for obtaining reliable regression forecasts, and the diagnostic tests that help improve model specification.
Our section on Forecasting Principles and Practices begins with Do Forecasting Methods Reduce Avoidable Error? Evidence from the Forecasting Competitions, Steve Morlidge’s careful and insightful look at what these so-called “competitions” tell us about choosing appropriate forecasting methods.
Nobel economist Paul Krugman wrote in 2009 that “[t]he economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.” David Orrell, author of Truth or Beauty: Science and the Quest for Order (2012), now asks whether this same hubris has applied to forecasters in general. In The Beauty of Forecasting, David concludes that because “living systems…resist the tidiness of mathematical laws,” it is a risky business indeed to assume that these systems we seek to analyze are either easily depicted or predictable through elegant equations.
Tao Hong concludes this section with an engaging historical overview of the objectives of energy forecasting and the evolution of the methodologies applied to this task – from simply counting lightbulbs in the earliest days of projecting load demands to the demand-response forecasting and renewable-generation forecasting required in the smart grid era. See his Energy Forecasting: Past, Present, and Future.