In the early 1980s, two of the authors of Principles of Business Forecasting, Robert Fildes and Keith Ord, were leaders in the movement to use experiments to determine which principles led to more accurate forecasts. The experiments show that forecasting methods today are capable of providing much more accurate forecasts that they were 50 years ago. This movement was a golden age for progress in forecasting. However, this knowledge has been not been widely adopted in business and government.
Scott Armstrong and Kesten Green are making a last call for help with their paper, "Forecasting methods and principles: Evidence-based checklists". They are ambitious for the paper: by the use of checklists, it is intended to make scientific forecasting accessible to all researchers, practitioners, clients, and other stakeholders who care about forecast accuracy.
Here is the abstract of the paper:
The Unscaled Mean Bounded Relative Absolute Error (UMBRAE) is a new way to measure forecast errors proposed, and well supported in, Chen, Twycross, and Garibaldi (2017). "A new accuracy measure based on bounded relative error for time series forecasting". The new measure appears to be a promising alternative, and is certainly worthy of further comparative research. Some analysts may want to continue using the RAE until further testing is done. We suggest using both measures in the meantime.
Don Miller and Dan Williams have recently re-posted spreadsheets and X-12 specifications that can be used to implement the seasonal damping method they proposed in "Shrinkage Estimators Of Time Series Seasonal Factors And Their Effect On Forecasting Accuracy," International Journal of Forecasting 19(4): 669-684, and "Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program," International Journal of Forecasting 20(4): 529-549.