We're actively looking for ideas on how to improve Weekly Challenges and would love to hear what you think!Submit Feedback
For those of you following along, thank you, you can find the solution to last week’s challenge (challenge #23) is HERE.
This week’s challenge will use the predictive time series tool called ARIMA. If you don’t have the predictive tools you can find the installer at http://downloads.alteryx.com/downloads.html look for the link to “Predictive tools only”. The predictive tools in Alteryx execute the analytics in an open source application called ‘R’, the advantage of using Alteryx vs. R is that Alteryx provides a straight-forward user interface and eliminates the need to program directly in the R language. If you want to read more about what is happening under the hood, here is a link to the Wiki on ARIMA. https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average
The use case: A retailer would like to forecast how many units of a particular product will be purchased from their locations based on a historical trend.
The source data contains weekly data for 2012 and 2013 details how many units have been moved. Some of the data, however, is populated with NULL values. For the NULL values, please assign the monthly average. If the monthly average is also NULL, assign the annual average.
Objective: Forecast the number of units that will be sold in the six weeks following the available data.
I arrived at the same answer, but I'm not sure it's the best answer. Based on the historical data, the forecast doesn't adjust for the drop off in sales each January. How would we configure the tool for a better answer? See spoilers below have the workflow plus what I came up when I exported to Tableau.
This is the data presented in Tableua plus the forecast solution that Tableau provided using the default settings.
My solution! I like these Time Series predictive tools, pretty intuitive (especially for someone who never uses predictive analytics!)
I've not really explored the TS Tools (except TS Filler), I ended up with the same solution as other however, my forecast scores are correct but the confidence scores are slightly out and I can't see why?