In the first of two posts spotlighting SAS R&D innovators, SAS' Udo Sglavo introduces you to developers Amy Shi, Maggie Du and Phil Helmkamp.
So you think you are smarter than the average forecaster, and can identify a trend in time series data? You now have a chance to put your trend detection skills (aka trendar) to the test, and help the cause of forecasting research in the process. Nikos Kourentzes, Associate Professor at
To properly evaluate (and improve) forecasting performance, we recommend our customers use a methodology called Forecast Value Added (FVA) analysis. FVA lets you identify forecasting process waste (activities that are failing to improve the forecast, or are even making it worse). The objective is to help the organization generate forecasts
