I've got data of which I think it has a seasonality. My data has a peak in july/august and one in december. I have only data of 2014 and 2015, but in both of the cases this is happening. (See my graph)

I tried to confirm this with autocorrelation, but this method only gives the so-called "White noise", probably because the seasons are 'irregular' (not one peak per year or something, it's not yearly, not quarterly etc.)

I need to forecast over this data, but I'm not sure if I should pick a model that uses seasonality or one that doesn't use seasonality.


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  • $\begingroup$ You can find this thread about detecting seasonality helpful: stats.stackexchange.com/questions/16117/… $\endgroup$ – Tim Feb 22 '16 at 13:36
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    $\begingroup$ There's nothing that can be done with seasonality if you have data for only one cycle. Unless, of course you have a very good theory behind the data. $\endgroup$ – Aksakal Feb 22 '16 at 14:31
  • $\begingroup$ @Aksakal I've got two cycles, 2014 and 2015, right? $\endgroup$ – Grafit Feb 22 '16 at 15:34
  • $\begingroup$ Ok, two cycles is better than one :) Just put the dummy variable on "summer" months. $\endgroup$ – Aksakal Feb 22 '16 at 15:50
  • $\begingroup$ @Aksakal Even when my autocorrelation doesn't show seasonality? Do you maybe have an example of a model with a dummy variable? Or could I just add it with a certain parameter to any model? $\endgroup$ – Grafit Feb 23 '16 at 8:13

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