I want to know how to understand whether my dataset, which I have plotted below using Excel, contains seasonality. Specifically:

  1. How to detect seasonality on viewing the chart

  2. How to detect seasonality from dataset (for coding purpose)

  3. If seasonality identified, how to remove the seasonality and make the dataset stationary.

enter image description here

Edit: new plot after transformation enter image description here


At a glance your data appears to potentially have seasonality. It is another question as to the kind of seasonality structure that is appropriate.

Data like yours can arise from a model that might include seasonal ARIMA structure or seasonal dummies ... the eye can't easily sort this out: Only the data knows for sure!

What I am suggesting is that the statistical characteristics of alternative models should be employed to evaluate these two alternative approaches. This can be done by evaluating tentative alternatives including seasonal dummies that might even change over time – e.g., a "January effect" that changed at a particular point in time.

Furthermore it is always possible that both forms of seasonality are needed to render an error process free of structure (i.e., gaussian). If you wish you can post your data and I will try to help further possibly just using visual methods. If your data is confidential simply scale it.


In this case the plot that would tell you the kind of seasonality that is present would be the ACF of the first differences.

enter image description here

  • $\begingroup$ Thank u for reply...My dataset is simple airline data. Could you specify the steps necessary to make the dataset stationary!.What I have done is log transformation and then differencing. The resultant plotted is the graph above. Which more transformation should I now use to make it stationary? What more should I do at this point. I am stuck at this for too long. Any guidance greatly appreciated. $\endgroup$ – Devi Nov 2 '17 at 6:37
  • $\begingroup$ How to remove seasonality from data? $\endgroup$ – Devi Nov 2 '17 at 10:10
  • $\begingroup$ Can seasonal adjustment method remove the said seasonality from the data ? $\endgroup$ – Devi Nov 2 '17 at 12:02
  • $\begingroup$ See autobox.com/cms/index.php/afs-university/intro-to-forecasting/… slide 62 ... for a modern discussion of the airline series. Logs are unnecessary if you treat 3 pulses. see stats.stackexchange.com/questions/18844/… as to when you need to take logs. Seasonality is a component/feature of a model. By filtering the data for the seasonality component , one can get series free of seasonality. The form and nature of the seasonality component depends on the data and is not fixed . $\endgroup$ – IrishStat Nov 2 '17 at 13:56
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    $\begingroup$ yes ... given that the series has been correctly differenced , the largest ACF value is often suggestive of the form of the seasonality. Care should be taken as monthly data OFTEN exhibits both a quarterly effect and a monthly effect. Quality analytics can discern the relative importance .. the human eye or scraping a graph NOT SO MUCH. $\endgroup$ – IrishStat Nov 3 '17 at 10:43

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