Forecasting During Chaos The Institute of Business Forecasting has produced an 80-minute virtual town hall on "Forecasting & Planning During the Chaos of a Global Pandemic." The on-demand video recording is available now and well worth a look. There is much solid practical guidance from an experienced panel: Eric Wilson,
Fildes and Goodwin (F&G) observed the subject (the regional subsidiary of a pharmaceutical company) was using a statistical forecasting system, but not fully trusting its output. Forecasters were making overrides to the system generated forecast to make it look like what they believed it should (e.g., following a life-cycle curve
Two weeks ago we looked at the first two steps in effecting forecasting process change: Justify your suspicions with data Communicate your findings That was the easy part. So why is it that so many organization realize they have a forecasting problem, yet are unable to do anything about it?
What if you suspect something is wrong with your forecasting process? What if the process is consuming too much time and too many resources, while still delivering unsatisfactory results (lousy forecasts). What can you do about it? This post looks at the first two steps to effecting meaningful forecasting process
With 2018's M4 Forecasting Competition behind us (although analysis, interpretation, and debate continue), the new M5 Competition starts March 2. Running through June 30, M5 is utilizing actual data provided by Walmart. It will be implemented using Kaggle's Platform, with $100,000 in prize money. Forecasting practitioners are encouraged to participate,
The International Journal of Forecasting has published its 2020-Q1 issue, guest edited by Spyros Makridakis and Fotios Petropoulos, and dedicated entirely to results and commentary on the M4 Forecasting Competition. This issue should be of great interest and value to business forecasting practitioners, and you get online access to it