Daniel - 3 months ago 8
R Question

# measure the best prediction method in R for a lot of time series

I'm struggling with a problem in R. I want to evaluate the best measure of prediction for a lot of time series with different methods, for example

`ME, RMSE, MAE, etc.`

The measured data are stored in a list, which saved these data frames:
`ME, RMSE, MAE`
... To evaluate the best method I thought about to sum up all values of the different methods and divide them by the length of
`residuen_overall`
.

For example by the length of two (ME):

(-1+0.26) / 2 --> -0.37

``````residuen_overall[[1]]
#      ME  RMSE  MAE    MPE  MAPE
#naive -1 10.15 3.93 -22.69 40.14

residuen_overall[[2]]
#        ME RMSE  MAE  MPE  MAPE
#naive 0.26 7.84 6.33 -3.1 19.61
``````

My idea was to create a loop, but it is not working:

``````methods <- list("ME", "RMSE", "MAE", "MPE", "MAPE")

for(i in 1:length(residuen_overall)) {
cumsum(residuen_overall[[i]]\$methods[i])
}
``````

I hope someone can help me, please! :)

Here are your data (if you use `dput()` in the future it will make it easier to reconstruct them for testing purposes):

``````residuen_overall <- list(
c(ME=-1,RMSE=10.16,MAE=3.93,MPE=-22.69,MAPE=40.14),
c(ME=0.26,RMSE=7.84,MAE=6.33,MPE=-3.1,MAPE=19.61))
``````

"Summing up all the values and dividing by the length" is just computing the mean value.

Collect your lists into a matrix:

``````r2 <- do.call(rbind,residuen_overall)
``````

Compute the mean of each column:

``````colMeans(r2)
##     ME    RMSE     MAE     MPE    MAPE
## -0.370   9.000   5.130 -12.895  29.875
``````

It's still not clear to me how this is going to help you figure out the best method ...