block by Kcnarf 8c462789ffbb04351a11

timeline - seasonality detection (II)

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This block is an experimentation of how to detect if a timeline has a seasonality component, and how to detect the lenght of the season (if any).

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 correlogram. This diagram represents all the coefficients of autocorrelation of the time serie (go to this block for detailed explanations of what is a coefficient of autocorrelation, and how to compute it). With the help of this diagram, one can identify season’s lenght, if any.

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