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・ Seasonal Agricultural Workers Program
・ Seasonal Attribution Project
・ Seasonal beer
・ Seasonal bonuses (Japan)
・ Seasonal breeder
・ Seasonal effects on suicide rates
・ Seasonal energy efficiency ratio
・ Seasonal flows on warm Martian slopes
・ Seasonal food
・ Seasonal human migration
・ Seasonal industry
・ Seasonal lag
・ Seasonal migration in Niger
・ Seasonal packaging
・ Seasonal spread trading
Seasonal subseries plot
・ Seasonal thermal energy storage
・ Seasonal tropical forest
・ Seasonal year
・ Seasonality
・ Seasonally adjusted annual rate
・ Seasoned equity offering
・ Seasoned Players
・ Seasoned salt
・ Seasoned tradeline
・ Seasoned Veteran
・ Seasoned Wood
・ Seasoning
・ Seasoning (colonialism)
・ Seasoning (cookware)


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Seasonal subseries plot : ウィキペディア英語版
Seasonal subseries plot

Seasonal subseries plots are a graphical tool to visualize and detect seasonality in a time series. Seasonal subseries plots involves the extraction of the seasons from a time series into a subseries based on a selected periodicity and are placed into mini time plots.
Seasonal subseries plots allows one to detect changes between different seasons , changes within a particular season over time as well as any underlying seasonal patterns. This plot is only useful if the period of the seasonality is already known. In many cases, this will in fact be known. For example, monthly data typically has a period of 12. If the period is not known, an autocorrelation plot or spectral plot can be used to determine it. If there is a large number of observations, then a box plot may be preferable.
==Definition==
Seasonal subseries plots are formed by
* Vertical axis: response variable
* Horizontal axis: seasons in chronological order. For example, with monthly data, all the January values are plotted (in chronological order), then all the February values, and so on.
The horizontal line displays the mean value for each month over the time series.
The analyst must specify the length of the seasonal pattern before generating this plot. In most cases, the analyst will know this from the context of the problem and data collection.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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