Identifying trend and seasonality of time series data
Another idea could be Fourier Transformation, which takes a time serie as an input (time domain), and converts it into frequency domain.

Consider this example:



When you transform the time series data from time domain into frequency domain, you can observe the repeated patterns (=seasonality). In this case, there is peak at 12 (day/night rhytm), at 24 (day) or 168 (week).

I can imagine you can perform the FFT and then extract the peaks based on certain threshold

The Git repo also contains a nice introduction into FFT.

Source: https://github.com/balzer82/FFT-Python