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How to explain Y axis values of plot_components for yearly, monthy, weekly. #876
How to explain Y axis values of plot_components for yearly, monthly, weekly graphs.
Question1: the values for y axis for the above 3 plots is different or same.
Question2: what scale are the y axis values ?
Can it be taken as the x% change in values or x times the amount added or subtracted
If you have additive seasonality (the default), then the values on the Y axis can be seen as the incremental effect on
If you use multiplicative seasonality, then the meaning will be the same but it will be in terms of a % instead of a raw number, and the axis label will actually show a % sign like in https://facebook.github.io/prophet/docs/multiplicative_seasonality.html
thank you for the reply.
to give some more background to what I am asking. my Y value is usually in thousands (19,000 ; 12,852, 4583, so on)
Another point where there is major confusion. Using Additional Seasonality
Model 1 (RMSE = 430.9) method = 'logistic', where there is no transformation done on Y, I get values of weekly between (-1.6 to 27.6).
Model 2 (RMSE 360.8) method= 'logistic', I take log transformation of Y and then the anti log of the fbprohept predict object to show the values in the graphs, here the value of weekly column does not convert back to the model 1 values or anywhere close.
There is something which I am missing or my fundamental understanding of the weekly values is wrong.
Can you please throw some light on this problem of mine.
If you post the
As for the log transform: If you take the inverse transform of each component, this will not give you the effect of the component in the untransformed space because log is a concave function. I give a discussion of that issue here: #647 (comment) . In (2), the exp() of the individual components now correspond to multiplicative seasonality, as described in my comment there.
Many thanks for the reply. Please find m.plot and m.plot_components
The Y value is in log. No inverse transformations have been done on the plots above.
But to show the results I take anti log and plot them and then create my own plots in tableau
finding the Y axis for weekly plot
After transformation and the above logic, similar plot to the one mode created
Would I be right in saying that on Monday there is a 1% change in the value of Y (5,438)
In the last plot there, the correct intepretation is that on Monday,
which is 0.2% seasonality.
Yeah, looking at the plot of the forecast, there really doesn't seem to be much weekly effect going on there. For instance if you look at 2017-11 through 2018-01, it's pretty much a straight line; there aren't any visible weekly oscillations in the time series.