Forum Discussion
How to calculate Forecast accuracy
Hi,
I previously marked this post as solved. In relation to the sample I sent over it was. However after trying to use this solution on my actual data I noticed that I could not put the pieces together. One of my issues is that the actual data is divided into two separet sheets (which is how I retreive the information). See example pictures below.
The actual sales is recorded per day
The Forecast is recorded per week and "Scenario" ( Scenario + Date = forecast for that week )
The forecast is recorded on the first day of the week
I want to be able to calculate the forecast accuracy on different time periods (week/Month/Quarter/Year). But when I try Seans solution above, I think I'm getting the variance for each day. Can I create new tables with calculated columns for Week/Month etc and then summarize the variances in that table, or is there some other way of doing this?
Please have a look at the example and see if you have better luck getting a grip on this.
ForecastSales
Thanks for the help!
Community ChampionThese links from SQLBI may be of some help:
http://www.daxpatterns.com/handling-different-granularities/
http://www.sqlbi.com/articles/budget-and-other-data-at-different-granularities-in-powerpivot/
There is also a good example on pp 378-381 of The Definitive Guide to DAX by Russo and Ferrari if you can get your hands on that book.
Thanks for the tip. Looked it through but I can't see anything that would help me. Most things are about measures, and from what I understand I should be using columns to be able to evaluate rows individualy.
Glad to receive any further insights or tips!
- dedelman_clng
A measure can be evaluated row by row if your visualization is used correctly (matrix/table with the row identifiers as the rows).
Hi again,
Do you think you could elaborate based on the two sample pictures? I'm not sure how to get to the next step even though I've been trying to read through various postes as well as the links you provided. I'm rather new at DAX as you probably understand at this point :)