I want to draw a graph of sales over time. However, the time periods I have available to me overlap. For example:

  • January 2012 to January 2013: 10 million
  • April 2012 to April 2013: 12 million
  • July 2012 to July 2013: 14 million
  • October 2012 to October 2013: 12 million

The obvious way to draw the graph is to do a simple line or bar chart, with the periods as labels on the y-axis:

enter image description here

(of course, the y-axis should begin at zero too!)

But this doesn't feel quite right, because it doesn't really show what's really going on - it looks like a discrete set of points, not an overlapping set of periods.

Is there a better way to visualise overlapping time periods?

  • 1
    $\begingroup$ The best way to graph this will depend on what assumptions you are willing to make. In particular, are you willing to assume that sales are not seasonal? $\endgroup$ – Peter Flom - Reinstate Monica Mar 12 '14 at 12:04
  • $\begingroup$ We need to visualise sales for a few different products, so it's difficult to think of assumptions that will work across all the products, I'm afraid. $\endgroup$ – Richard Mar 12 '14 at 16:09

Real answer: Get better data. When it comes to sales it's difficult to say anything remotely useful if you don't have at least monthly data, as most sales fluctuate with season and holidays, which cluster at some particular months.

The fact that the annual running mean goes up and down implies that the sales is not constant across year. I'm unsure about your analysis question, but I'd at least suggest starting to collect data in finer level such as daily or weekly.

Less useful answer: Yes, you can still plot something and get some insight. Like this chart (made using MS Excel):

enter image description here

You can see that sales seems to be the highest at Span 3, which centers at major holidays such as New Year and Christmas. But all of these "trends" can be a mixture of real seasonal trend or just annual fluctuation. I wouldn't draw any conclusion with only these four annual data points.

An improved version as suggested by Peter:

enter image description here


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