In this blog we briefly cover some key insights for successful time series forecasting: (a) Profiling the Shape of the Curve is the first stage, and the first step is assessing if the time series is stationary. (b) The forecast method identified must capture the shape and be able to project the shape across time. (c) There are limits in historical and no amount of “fancy math” can overcome them.
Insight from Applied Statisticians for Forecasting: Is It Worth the Effort and the Mirage of Random Variation?
In this blog, we will illustrate through an example of these potential pitfalls (unanchored, random variation, and narrow metrics) and potential negative impact on a firm.
How to use demand planning statistical models to enhance the value of your sales input during the forecasting process.