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I have been workin with time-series data. I haven't been able to find any way of analysing automatically if a given time-series has a seasonal behaviour (when I say automatically, I mean in a way I can program an algorithm to take the time-series as an input and return a True or False, instead of someone having to manually analyse the graphical representation).

Does anyone have any code suggestions (preferably in Python) or references to good papers on that matter? If it is applied to big data even better, but if not anything will help.

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I recommend looking at the forecast package for R, which is pretty much the gold standard in time series forecasting. Specifically, the auto.arima() function decides on whether to use seasonal differencing (and how many differences to take), based on an estimate of seasonal strength per this paper:

 Wang, X, Smith, KA, Hyndman, RJ (2006) "Characteristic-based
 clustering for time series data", _Data Mining and Knowledge
 Discovery_, *13*(3), 335-364.
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