I'm doing some electricity load forecasting in which I've used 5 fold cross validation and have calculated MAPE for each split as follows:

NIC 12.4070736159999    12.4381016317022    13.012084025233 12.8202279490414    13.0173158393873
QLD 11.1222557214741    11.2011253786453    11.0949104146992    11.0204844071916    10.9866043178404
SA  18.1933345652622    16.5824118552869    16.9662739986567    22.0912790309511    18.7201687363193
TAS 10.9283795353769    10.8375790347786    10.9969285266692    10.65564127531  10.830705163829
VIC 14.4304582955302    13.749822370597 14.185836762341 14.1723784565888    14.8015564381059

I want to show the results in my research paper but I don't know how to present the results. I want to know, other than showing MAPE for each folds what else is shown in the paper? (like standard deviations of the error, confidence interval etc)


Standard forecasting papers unfortunately usually only show the averages of errors, so you would show the averages of your MAPEs.

The authors often then start to discuss differences in the third significant digit. Without a notion of the variation in errors, this makes no sense. Therefore, I very much recommend that you do indicate the variation in your errors, e.g., by giving standard deviations.

In addition, it is common practice in (load and other) forecasting papers to present results on multiple error measures, e.g., the or the in addition to the .

I suggest you skim though a couple of load forecasting papers and be inspired by what you find there.

For your specific data, a nice and useful visualization could be a dotchart like this (note how I jittered the dots horizontally to reduce overplotting):


mapes <- structure(c(12.4070736159999, 11.1222557214741, 18.1933345652622, 
10.9283795353769, 14.4304582955302, 12.4381016317022, 11.2011253786453, 
16.5824118552869, 10.8375790347786, 13.749822370597, 13.012084025233, 
11.0949104146992, 16.9662739986567, 10.9969285266692, 14.185836762341, 
12.8202279490414, 11.0204844071916, 22.0912790309511, 10.65564127531, 
14.1723784565888, 13.0173158393873, 10.9866043178404, 18.7201687363193, 
10.830705163829, 14.8015564381059), .Dim = c(5L, 5L), .Dimnames = list(
    c("NIC", "QLD", "SA", "TAS", "VIC"), NULL))

xx <- runif(nrow(mapes)*ncol(mapes),-0.3,0.3)+rep(1:ncol(mapes),nrow(mapes))

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