Automatic detection of seasonality on a time-series 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.
 A: Section 3.2 in the following paper offers a possibility for determining the length of the seasonal cycle:
 Wang, X, Smith, KA, Hyndman, RJ (2006) "Characteristic-based
 clustering for time series data", _Data Mining and Knowledge
 Discovery_, *13*(3), 335-364.

However, note that this was never included in the forecast::auto.arima() function (whose author is Hyndman), although this does use other methods from that paper (for instance, auto.arima() decides whether to apply seasonal differencing for known seasonal cycle length based on an estimate of seasonal strength as also given in Wang et al.).
I do not now why this was never included. It may have been because it was unstable, varying and hard to automate. After all, you need to identify peaks and troughs in the ACF, and what constitutes a "peak" or a "trough" in a noisy ACF series would need to be operationalized.
Alternatively, perhaps there simply never was any demand for it, since users presumably know their seasonal cycle length.
So if you want to use the cycle length determination per Wang et al., you would need to code it yourself.
