# Understanding the period/cycle of time series data

I'm trying to understand the meaning of period/cycle length in time series forecasting. Some functions, such as seasonal_decompose and STL (Python statsmodels package) or models like SARIMA have a period or cycle parameter that indicates 'the period of the series' used (period, seasonal, etc). As I understand it, this would be the period after which the seasonal behaviour of the series repeats (according to this answer).

I'm working with web traffic data that was recorded in 1hour intervals over the span of 8-14 months. My layman's fourier analysis seems to indicate that the data's strongest underlying frequencies are: 24h, 335h (14 days), 675h (28 days), 3720h (155 days), 6h and 12h (in order).

Most online examples assume only 1 frequency in the data, usually as a yearly reoccurrence, and there seems to be little mention of special treatment for time series that have more than a yearly seasonal component. Is such special attention to multiple seasonalities commonly practiced? Is there pre-built code specifically designed for this or are 'single' period functions adapted in some way?