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timeline - seasonality detection (I)

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If you suspect a time serie has a seasonality component, this is an example of how to validate/invalidate this hypothesis.

Seasonality means that the time serie has a periodic component, repeating the same pattern on each period. For example, sales of a store may have a week-based seasonality: sales increase on saturday, while there is no sale at all on sunday.

Graphically speaking, detecting a seasonality is (quite) easy: just look for a repeating pattern. Note that it could be difficult if the pattern has a long period, or/and the order of magnitude of the seasonilaty is low (ie. lowest and highest values are not so far from the season’s mean, but in this case there might be no seasonality at all ! ).

Computationnaly speaking, one can use the autocorrelation technique. Correlation is a technique that allows to determine if 2 time series are correlated (ie. if they behave in the same way). Autocorrelation is the same technique applied to only 1 time serie: it allows to compare the correlation between a time serie and the same time serie shifted by a certain amount of time. Hence, if you suspect a seasonality of 7 days, then the autocorrelation between the time serie and the same time serie shifted by 7 days will validate (autocorrelation near 1) or invalidate (autocorrelation near 0) the seasonality.

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