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.

We have added Ev Gardner's spreadsheet for damped trend exponential smoothing to the Software Page.

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.

Scott Armstrong and Kesten Green are seeking suggestions of relevant experimental evidence that they have overlooked in their new working paper, "Demand Forecasting II: Evidence-based methods and checklists". They describe the problem that the paper addresses as follows:

Decision makers in the public and private sectors would benefit from more accurate forecasts of demand for goods and services. Most forecasting practitioners are unaware of discoveries from experimental research over the past half-century that can be used to reduce errors dramatically, often by more than half. The objective of this paper is to improve demand forecasting practice by providing forecasting knowledge to forecasters and decision makers in a form that is easy for them to use.

The paper is available from ResearchGate, here.