In their new paper, Scott Armstrong, Kesten Green, and Andreas Graefe present a unifying theory of forecasting in the form of a Golden Rule of Forecasting. The rule is to be conservative. Being conservative in forecasting means applying all cumulative knowledge about the forecasting problem and using evidence-based methods.

You are invited to participate in a web-based judgmental forecasting exercise. You will be asked to select the best forecasting model, based on your judgment, for 32 time series.

The exercise consists of four rounds. Each round will contain 8 series and will be followed by a short questionnaire, while different types of information will be provided in addition to forecasts. Detailed instructions are provided prior to each round. You may complete the exercise in one session, or you can save your work to complete it later at your convenience.

At the end of the exercise you will be provided with your score. Your final performance will be calculated based on the rank of the models corresponding to your choices, according to the actual (out-of-sample) performance of each model. If your score is one of the top 20, you will be awarded an Amazon Gift Card valued at £50.

You can access the web-based exercise here:

The U.S. Intelligence Advanced Research Projects Activity (IARPA) has issued a Request for Information on "Lessons Learned Knowledge Management" that may be of interest to forecasting researchers. For more information, see the RFI here.

The co-directors of the site are pleased to announce that the Forecasting Principles Internet site has exceeded a total of 10 million visitors since April 2001. The site provides information on evidence-based forecasting for researchers and practitioners as a public service. The visitor numbers are one measure of the value of the service; another is the site's first place (non-encyclopedia) ranking in a Bing search for the word forecasting.

The Golden Rule of Forecasting, Be Conservative, has been proposed by Scott Armstrong, Kesten Green, and Andreas Graefe. The authors are seeking reviews of their paper. The paper is available from here. Have they overlooked relevant research; perhap yours? If you can help, please contact This email address is being protected from spambots. You need JavaScript enabled to view it..