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Hierarchical and Grouped Time Series

Home Page: http://pkg.earo.me/hts

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A typo

In the Usage, "demo(infangts) # gts" should be "demo(infantgts) # gts". There is a "t" missed.

Missing nodes (time-series) in hierarchy: combinef reconciliation function error

I am trying to reconcile a set of time series forecast that are already in hierarchal format using combinef.
Issue is there are some missing node (time series) for example A -> AA AB, B -> BA BB, AA-> AAA AAB, BB -> BBA BBB.
As you notice in this example there is not series for AB. In this situation the combinef function is erroring out.
Not sure it should be a new issue or this is an existing issue.
Error in utmat %*% fcasts: Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90
Traceback:

combinef(fcasts, nodes, weights = NULL, algorithms = "cg",
. keep = "gts")
CG(fcasts = fcasts, S = smat, weights = weights)
as.matrix(utmat %*% fcasts)
utmat %*% fcasts
utmat %*% fcasts

parallel doesnt work on custom functions

forecast.gts(
htseg1, h = 10, method = "comb", algorithms = "lu",weights = "mint",
FUN = function(x) snaive(x), parallel = T,num.cores = 2
)

hts cannot create a hierarchical time series for a single observation

It seems that hts cannot create a hierarchical time series if we have only one observation in the series.
It returns: Error in if (nbts <= 1L) { : argument is of length zero

library(hts)

nodes <- list(2, c(3, 2))
abc <- ts(5 + matrix(sort(rnorm(500)), ncol = 5, nrow = 100))
y <- hts(abc[1,], nodes)

Missing combinations in grouped time series causes error

See
http://stackoverflow.com/q/40729669/144157

error when installing `hts` package from CRAN and github

install.packages('hts', dependencies = TRUE)
#> 
#>   There is a binary version available but the source version is
#>   later:
#>     binary source needs_compilation
#> hts  5.1.0  5.1.4              TRUE
#> installing the source package 'hts'
#> Warning in install.packages("hts", dependencies = TRUE): installation of
#> package 'hts' had non-zero exit status

Particular warnings (not in reprex output):

ld: warning: directory not found for option '-L/usr/local/gfortran/lib/gcc/x86_64-apple-darwin15/6.1.0'
ld: warning: directory not found for option '-L/usr/local/gfortran/lib'
ld: library not found for -lgfortran

session info

sessionInfo()
#> R version 3.4.0 (2017-04-21)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS Sierra 10.12.5
#> 
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] compiler_3.4.0  backports_1.1.0 magrittr_1.5    rprojroot_1.2  
#>  [5] tools_3.4.0     htmltools_0.3.6 yaml_2.1.14     Rcpp_0.12.11   
#>  [9] stringi_1.1.5   rmarkdown_1.6   knitr_1.16      stringr_1.2.0  
#> [13] digest_0.6.12   evaluate_0.10.1

A typo in the code causes error when calling forecast.gts() with nonnegative=T parameter

Hi,

I have created a grouped time series with 2 groups and 3 levels in each. When I pass this data to forecast.gts function error pops up saying:

Error in forecast.gts(gtsdf, h = 5, method = "comb", fmethod = "arima", :
object 'fcasts' not found

the code for creating ts:

gtsdata <- gts(tsdata, characters=list(c(6,2,3),c(2,1,1)))
modelFcsts<-forecast.gts(gtsdata, h=5, method="comb", fmethod="arima", weights='mint', covariance='shr', keep.fitted = T, keep.resid = T, nonnegative = T)

I've been digging a bit through the source code and I think the issue is in line 281 in [https://github.com/earowang/hts/blob/master/R/forecast-gts.R]
where it should state: pfcasts[pfcasts < 0] <- 0

Fully reproducible example on the public data that can be used to recreate problem is here (I've used the example from [https://otexts.com/fpp2/gts.html] with small adjustment to get negative forecasts):

library(fpp2)
library(hts)

prisonNeg<-prison
prisonNeg[,1]<-seq(nrow(prisonNeg), 1, -1)         # just changing this column to get negative predictions from the model
prison.gts <- gts(prisonNeg, characters = c(3,1,9),  gnames = c("State", "Gender", "Legal",  "State*Gender", "State*Legal",  "Gender*Legal"))
modelFcsts<-forecast.gts(prison.gts, h=5, method="comb", fmethod="arima", weights='mint', covariance='shr', keep.fitted = T, keep.resid = T, nonnegative = T)

GTS object plotting - label inconsistencies.

Here is a slightly altered example from the help docs:

bnames1 <- c("VICMelb", "VICGeel",
             "NSWSynd", "NSWWoll")
bts1 <- matrix(ts(rnorm(160)), ncol = 4)
colnames(bts1) <- bnames1
x1 <- gts(bts1, characters = c(3, 4))
x1$groups
plot(x1)

From there, we can see in the graph that the third level isn't titled Bottom, and the series there all begin with G1/... instead of Bottom/... or G1/VIC/... and G1/NSW/.... I don't imagine that was what was intended (but I could be wrong).

Additionally, it doesn't appear I can rename the Bottom level; the gnames argument to gts() in this case only accepts a character vector of length 1 and applies that to the middle level. Is this intended?

> sessionInfo()
R version 3.1.1 (2014-07-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] hts_4.4          forecast_5.6     timeDate_3010.98 zoo_1.7-11      

loaded via a namespace (and not attached):
 [1] colorspace_1.2-4 fracdiff_1.4-2   grid_3.1.1       lattice_0.20-29  nnet_7.3-8       parallel_3.1.1   quadprog_1.5-5  
 [8] Rcpp_0.11.2      SparseM_1.05     tools_3.1.1      tseries_0.10-32

Allow more general base models

Allow tbats as a base model.
Also we could have any function that takes a time series returns the point forecasts for each series within forecast.gts.

Error in install.packages : cannot open the connection

I am trying to install hts package on windows R machine and I am getting few warnings and then it crashes.

Warning in install.packages :
downloaded length 208896 != reported length 1320640
Warning in install.packages :
error 1 in extracting from zip file
Warning in install.packages :
cannot open compressed file 'hts/DESCRIPTION', probable reason 'No such file or directory'
Error in install.packages : cannot open the connection

When I try to manually download this package from your website, it also says broken link.

Forecast.gts() does not tell us which bottom level series throws the error

Here is the code I am running -
fit <- forecast.gts(new_ts,h = 365,fmethod = "arima",xreg = train,newxreg = holidayf$Total,method = "comb")

Error in auto.arima(x, lambda = lambda, xreg = xreg, parallel = FALSE, : No suitable ARIMA model found In addition: Warning message: In auto.arima(x, lambda = lambda, xreg = xreg, parallel = FALSE, : Unable to calculate AIC offset
It would massively help if I knew which bottom level series is causing the error so that I can isolate it.

`is.hts` and `is.gts` functions are not exported

It seems that the is.hts and is.gts functions are not exported but I think that they are useful during code development (at least for me :) ).

Interop with tidyverts

Hi,

This is some great work here. I noticed though, that it's still based on the forecast framework. Are there any plans to port it to tidyverts or make a replacement? Or has this been done already?

`xreg` issue when having user-defined forecast function

I was trying to use forecastHybrid with hierarchical time series and external regressors but I cannot figure out how to do that. I am trying in this way:

library(hts)
library(forecastHybrid )

forecast(htseg1, a.args = list(xreg = data.frame(1:10)), models = "aet", 
xreg = data.frame(11:20), FUN = hybridModel)

But I got the following error:

Error in forecast.Arima(object$auto.arima, h = h, xreg = xreg, level = level) : No regressors provided

Hts objects don't return frequency of their embedded mts object.

frequency(my.hts) seems to return 1 even if frequency(my.hts$bts) returns 12. Looks like an error because the forecast.hts() method looks at the frequency of the hts object to determine the default forecast horizon. See the slightly modified stock example below.

> require(hts)
> abc <- ts(5 + matrix(sort(rnorm(1600)), ncol = 16, nrow = 100),frequency=12)
> sex <- rep(c("female", "male"), each = 8)
> state <- rep(c("NSW", "VIC", "QLD", "SA", "WA", "NT", "ACT", "TAS"), 2)
> gc <- rbind(sex, state) # a matrix consists of strings.
> gn <- rbind(rep(1:2, each = 8), rep(1:8, 2)) # a numerical matrix
> rownames(gc) <- rownames(gn) <- c("Sex", "State")
> x <- gts(abc, groups = gc)
> frequency(x)
[1] 1
> frequency(x$bts)
[1] 12
> sessionInfo()
R version 3.1.1 (2014-07-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] hts_4.4          forecast_5.6     timeDate_3010.98 zoo_1.7-11      

loaded via a namespace (and not attached):
 [1] colorspace_1.2-4 fracdiff_1.4-2   grid_3.1.1       lattice_0.20-29  nnet_7.3-8       parallel_3.1.1  
 [7] quadprog_1.5-5   Rcpp_0.11.2      SparseM_1.05     tools_3.1.1      tseries_0.10-32

Label crop in plot

Labels get cropped in plots of an hts object. This is especialy a problem when handling lengthy label names. See http://stackoverflow.com/questions/22387811/r-hts-package-label-crop-in-plot

combinef return

Hello,
I already have forcast data, when I use combinef to revise the forcast, return this error. The input format is the same as the example of function combinef(). What cause this error?

as.matrix(dat)
Time Series:
Start = 1
End = 2
Frequency = 1
Total yCN01 yCN02 y755Y y023Y y010A y755AC y755AG y023Y00001 y010AAC
1 120 90 20 41 55 15 19 20 55 10
2 201 139 89 61 87 100 29 55 87 115
gtnode
$level 0
[1] 2

$level 1
[1] 3 1

$level 2
[1] 2 1 1

com <- combinef(fcasts=as.matrix(dat), nodes=gtnode, weights = NULL, keep = "all", algorithms = "lu")
Error in rowsum.default(repcount, rep(1L:nlist[i], xlist[[i]])) :
incorrect length for 'group'

Getting fitted and residual values when using comb nonnegative method

Would be great if hts returned fitted and residual values when using comb nonnegative method

`TdFp` not working properly for a hierarchy with two levels

See http://stackoverflow.com/questions/43455547/how-to-do-top-down-forecasted-proportions-for-hts-objects-with-2-levels

Allow multiple seasonal objects in hts() and gts()

Currently a multiple seasonal object is converted to ts within hts and gts. This precludes the use of tbats for forecasting. We should be able to leave msts and ts objects as they are, and only convert to ts if the y has no time series characteristics.

Failed to install 'hts' due to RcppEigen.h

When trying to install 'hts' on a Ubuntu 18.04.6 LTS AWS server
with R version 3.4.4 using this command

sudo R -e 'install.packages("hts", dependencies = TRUE)'

the installation breaks because RcppEigen is not found

These are the logs

ubuntu@ip-172-31-16-51:~$ sudo R -e 'install.packages("hts", dependencies = TRUE)'

R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> install.packages("hts", dependencies = TRUE)
Installing package into ‘/usr/local/lib/R/site-library’
(as ‘lib’ is unspecified)
trying URL 'https://cloud.r-project.org/src/contrib/hts_6.0.2.tar.gz'
Content type 'application/x-gzip' length 821684 bytes (802 KB)
==================================================
downloaded 802 KB

* installing *source* package ‘hts’ ...
** package ‘hts’ successfully unpacked and MD5 sums checked
** libs
g++  -I/usr/share/R/include -DNDEBUG  -I"/usr/local/lib/R/site-library/Rcpp/include" -I"/usr/local/lib/R/site-library/RcppEigen/include"    -fpic  -g -O2 -fdebug-prefix-map=/build/r-base-AitvI6/r-base-3.4.4=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -g  -c RcppExports.cpp -o RcppExports.o
RcppExports.cpp:4:10: fatal error: RcppEigen.h: No such file or directory
 #include <RcppEigen.h>
          ^~~~~~~~~~~~~
compilation terminated.
/usr/lib/R/etc/Makeconf:168: recipe for target 'RcppExports.o' failed
make: *** [RcppExports.o] Error 1
ERROR: compilation failed for package ‘hts’
* removing ‘/usr/local/lib/R/site-library/hts’

The downloaded source packages are in
	‘/tmp/RtmpO7eOeq/downloaded_packages’
Warning message:
In install.packages("hts", dependencies = TRUE) :
  installation of package ‘hts’ had non-zero exit status
> 
>

Parallel processing in forecast.gts(), when using external variables for arima

See this post

Sample covariance calculation

Thank you for this package! I'd like to confirm the sample covariance calculation in the "MinT" function here: https://github.com/earowang/hts/blob/master/R/MinT.R#L184

n <- nrow(res)
w.1 <- crossprod(res) / n

I expected w.1 to be the usual sample covariance matrix instead:

w.1 <- cov(allr)

# same as
n <- nrow(res)
res.centered <- scale(res, center=TRUE, scale=FALSE)
crossprod(res.centered)/(n-1)

The difference is that the usual sample covariance matrix (1) centers the residuals and (2) divides by n-1 instead of n for an unbiased estimate.

Do you not center residuals because you assume base forecasts are unbiased? This seems to be the intent from eqn3 and the formula given in lemma 1. https://robjhyndman.com/papers/mint.pdf
Is the the choice to normalize by n instead of n-1 also intentional?

Thank you for confirming!

Enhancing hts for fmethod nnetar

Currently hts package is limited to arima,ets, and rw forecasting methods. Can we extend it to nnetar?
It would be helpful, as we include neural network in hierarchical methods.

Allow xreg and newxreg to take different values for each series in forecast.gts()

Currently, the following code will work:

htseg2x <- matrix(rnorm(16*17),nrow=16,ncol=17)
htseg2nx <- matrix(rnorm(10*17),nrow=10,ncol=17)
forecast(htseg2 , h=10, fmethod="arima", xreg=rnorm(16), newxreg=rnorm(10))

But this only allows the same xreg and newxreg vectors to be applied to all time series. We need to allow a matrix to be passed for each argument.

combinef not working with only two levels

Hi,
I'm facing a problem with a really simple model having only two hierarchical levels (root + one level).

Here's an example to reproduce the problem:

A <- matrix(c(5,5,5,5,2,2,2,2,2,2,2,2), 4,3)
Nodes <- list()
Nodes[["Level 0"]] <- 2
Weights <- c(1,10,10)

rbasedfcst <- combinef(fcasts=A, nodes=Nodes,
                       weights = Weights,
                       keep = "gts")

that raises this error:

Error in colnames<-(*tmp*, value = unlist(labels[length(labels)])) :
length of 'dimnames' [2] not equal to array extent

If I try with 3 or more levels it seems to work perfectly, e.g. :

A <- matrix(c(5,5,5,5,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1), 4,7)
Nodes <- list()
Nodes[["Level 0"]] <- 2
Nodes[["Level 1"]] <- c(2,2)

Weights <- c(1,10,10,10,10,10,10)

rbasedfcst <- combinef(fcasts=A, nodes=Nodes,
                       weights = Weights,
                       keep = "gts")

The error seems located in the private function HierName.

Environment :

R version 3.1.3
hts version 4.5

readme nit

Just noting for the README file that the arguments to install from GitHub are in the wrong order. Should be

install_github("hts","robjhyndman")

How to work on a single level hierarchy?

I was wondering how I could specify a one-level hierarchy, for instance, a single State divided in Cities( 7 digits id ).
I was trying to pass the city argument as "characters=c(7)" and unfortunately I am getting the following outcome

data.hts<-hts(data.ts, bnames = colnames(data.ts),characters=c(7))
Error in substr(unique.str[[x]], start = 1L, stop = end[x - 1L]) :
invalid substring arguments

Error in hts(...) .Internal(nchar) ...

I repeatedly get an annoying error with the hts() function even for the example in the help.

http://stackoverflow.com/questions/31753779/error-with-hts-package

Allow hts function to take forecasts as an input for top down and bottom up approaches.

Export the top-down method functions

Predictions are always flat?

Hello, I'm new to the hts package so I apologize for any stupid questions I may have. For some reason, regardless of which method or fmethod I use, my forecasts always looks similar to this:

In this case, the code is:

fcast<-forecast(train. hts,h=101, fmethod='ets', method="tdgsa")

This is the summary of the hts object:

> train.hts

Hierarchical Time Series
4 Levels
Number of nodes at each level: 1 5 10 120
Total number of series: 136
Number of observations per series: 505
Top level series:
Time Series:
Start = 1
End = 505
Frequency = 1

Is there an obvious reason why the forecast is just the same value for each time period?
If more information is required to provide an answer, please let me know
Thanks and have a good day,

combinef returns negative reconciled forecasts

Is there any way of using weights or computing weights in combinef function so that they do not return negative forecasts in lower levels?

combinef function to constrain bottom level predictions with external predictions in gts

Hello,
I have a population dataset with 3 grouping variables (Age, Gender and Urban/Rural Setting).
I would like to generate bottom level predictions ( for Age * Gender * UrbanRuralSetting) and to make them match with higher level predictions (Age*Gender) that we have from external sources (outputs of a model).
I am discovering the hts package, but I think this is something I can do with the combinef function. Is it correct?
Is there a better way to proceed with the functions provided by the package ? I was initially thinking to use an arima model with the higher levels as external predictors (following this tutorial : https://robjhyndman.com/hyndsight/hts-with-regressors/), but I have read that external predictors should be other explanatory variables, and not predictions. Do you confirm this point? I'll be happy to provide a reproductible example once I am sure of the function I should use.
Best regards,
Améline Vallet

Add prediction intervals

The equations are given in the CSDA 2011 paper. Can we add prediction intervals as an optional output to forecast.gts?

Implement Hyndman's fix for non conforming forecast

sensible_forecasts <-
  function(forecasts,
           force_signal = c("positive", "negative"),
           max_iterations = 1000,
           threshold = 0,
           nodes,
           groups,
           weights = NULL,
           algorithms = c("lu", "cg","chol", "recursive", "slm"),
           prose = FALSE)
  {
counter = 1
if (force_signal == "positve") {
  comparisson = `>=`
  rounding_function = ceiling
} else {
  comparisson = `<=`
  rounding_function = floor
}

transformation <- function(x)  ifelse(comparisson(x,0),x,0) 


repeat {
forecasts <- transformation(forecasts)  
y_f <- combinef(forecasts,nodes,groups,weights,algorithms)
forecasts <- aggts(y_f) %>% rounding_function()
counter = counter + 1
if (all(comparisson(forecasts,threshold)) | counter == max_iterations ) {
  forecasts <- transformation(forecasts)
  if (prose == TRUE) {print(paste0("Number of attempts", counter)) }
  break
  }
}
return(forecasts)
}

Extension of createNotes() to work with levels specified by seperators

Hello,

sorry for my novice behaviour regarding pull requests and so on.

I'm opening an issue for this as I've announced:

It would be great if nodes could be specified by a separator such as an underscore.

This way the function could handle different length of nodes names.

My current solution looks for hts.R can be found in my fork repo. This time I only use function from base R:

panoptikum@1d1ee22

I've tested with the examples:

abc <- ts(5 + matrix(sort(rnorm(1000)), ncol = 10, nrow = 100))
colnames(abc) <- c("AA_100_A_172", "AA_100_A_172", "A_10_C_A", "A_2_B_21", "A_2_B_DA","B30_A_1_H", "B30_B_3_Z", "B30_B_1_%", "B_40_A_2", "B_40_A_3")
y <- hts(abc, characters = c(1, 2, 1), sep="_")

and

abc <- ts(5 + matrix(sort(rnorm(1000)), ncol = 10, nrow = 100))
colnames(abc) <- c("AA_100_A", "AA_10_B1Z2", "A_10_C", "A_2_AB", "A_2_B","B30_A_1", "B30_B_3", "B30_CA_1", "B_40_A", "B_40_B")
y <- hts(abc, characters = c(1, 2, 1), sep="_")

gave me for y$nodes:

$`Level 1`
[1] 4

$`Level 2`
 AA   A B30   B 
  2   3   3   2 

$`Level 3`
AA100   A10    A2  B30A  B30B   B40 
    2     1     2     1     2     2 

$`Level 4`
AA100A   A10C    A2B  B30A1  B30B3  B30B1   B40A 
     2      1      2      1      1      1      2

and respectively:

$`Level 1`
[1] 4

$`Level 2`
 AA   A B30   B 
  2   3   3   2 

$`Level 3`
AA100  AA10   A10    A2  B30A  B30B B30CA   B40 
    1     1     1     2     1     1     1     2

Best

Bad grouping when colnames is to large

When calling gts over a large colname the grouping fails to mapply since duplicates are dropped.

Lets begin with the following data.frame

bnames1 <- c("CO0993W01010011388888NA0001TF001001XXXXXXXPR0011", 
             "CO0993W01110011388888NA0001TF001002XXXXXXXPR0012", 
             "CO0994W02210011388888NA0001TF001003XXXXXXXPR0013", 
             "CO0994W02310011388889NA0002TF001004XXXXXXXPR0014",
             "RE0995W01410011388889NA0002TF001005XXXXXXXPR0015", 
             "RE0995W02510011388889NA0003TF001006XXXXXXXPR0016", 
             "RE0996W03110011388889NA0003TF001007XXXXXXXPR0017", 
             "WA0997W01210011388889NA0003TF001008XXXXXXXPR0018",
             "WA0998W01310011488898NA0004TF001009XXXXXXXPR0019", 
             "WA0999W03410011488898NA0004TF001010XXXXXXXPR0010", 
             "FL0983W01510011488898NA0004TF001011XXXXXXXPR0011", 
             "FL0983W02110011488878NA0005TF001012XXXXXXXPR0011",
             "FL0983W02210011488878NA0005TF001013XXXXXXXPR0013", 
             "IC0973W01310011488878NA0005TF001014XXXXXXXPR0011", 
             "IC0972W01410011488878NA0006TF001015XXXXXXXPR0011", 
             "IC0972W01510011488878NA0006TF001016XXXXXXXPR0011")
bts1 <- matrix(ts(rnorm(160)), ncol = 16)
colnames(bts1) <- bnames1
x1 <- gts(bts1, characters = list(c(2, 4, 3, 4), c(3, 5, 6, 15, 6)))

This will generate the following warning:

Warning message:
In mapply(rep, as.list(gnames), times, SIMPLIFY = FALSE) :
  longer argument not a multiple of length of shorter

And the following groups with NA assignments

$G1
[1] "G1/CO" "G1/RE" "G1/WA" "G1/FL" "G1/IC"

$G2
 [1] "G2/CO0993" "G2/CO0994" "G2/RE0995" "G2/RE0996" "G2/WA0997" "G2/WA0998" "G2/WA0999"
 [8] "G2/FL0983" "G2/IC0973" "G2/IC0972"

$G3
 [1] "G3/CO0993W01" "G3/CO0994W02" "G3/RE0995W01" "G3/RE0995W02" "G3/RE0996W03" "G3/WA0997W01"
 [7] "G3/WA0998W01" "G3/WA0999W03" "G3/FL0983W01" "G3/FL0983W02" "G3/IC0973W01" "G3/IC0972W01"

$G4
 [1] "G4/CO0993W010100" "G4/CO0993W011100" "G4/CO0994W022100" "G4/CO0994W023100"
 [5] "G4/RE0995W014100" "G4/RE0995W025100" "G4/RE0996W031100" "G4/WA0997W012100"
 [9] "G4/WA0998W013100" "G4/WA0999W034100" "G4/FL0983W015100" "G4/FL0983W021100"
[13] "G4/FL0983W022100" "G4/IC0973W013100" "G4/IC0972W014100" "G4/IC0972W015100"

$G5
[1] "G5/113" "G5/114"

$G6
[1] "G6/11388888" "G6/11388889" "G6/11488898" "G6/11488878"

$G7
[1] "G7/11388888NA0001" "G7/11388889NA0002" "G7/11388889NA0003" "G7/11488898NA0004"
[5] "G7/11488878NA0005" "G7/11488878NA0006"

$G8
 [1] "G8/11388888NA0001TF001001XXXXXXX" "G8/11388888NA0001TF001002XXXXXXX"
 [3] "G8/11388888NA0001TF001003XXXXXXX" "G8/11388889NA0002TF001004XXXXXXX"
 [5] "G8/11388889NA0002TF001005XXXXXXX" "G8/11388889NA0003TF001006XXXXXXX"
 [7] "G8/11388889NA0003TF001007XXXXXXX" "G8/11388889NA0003TF001008XXXXXXX"
 [9] "G8/11488898NA0004TF001009XXXXXXX" "G8/11488898NA0004TF001010XXXXXXX"
[11] "G8/11488898NA0004TF001011XXXXXXX" "G8/11488878NA0005TF001012XXXXXXX"
[13] "G8/11488878NA0005TF001013XXXXXXX" "G8/11488878NA0005TF001014XXXXXXX"
[15] "G8/11488878NA0006TF001015XXXXXXX" "G8/11488878NA0006TF001016XXXXXXX"

$G9
 [1] "G9/11388888NA0001TF001001XXXXXXXPR0011" "G9/11388888NA0001TF001002XXXXXXXPR0012"
 [3] "G9/11388888NA0001TF001003XXXXXXXPR0013" "G9/11388889NA0002TF001004XXXXXXXPR0014"
 [5] "G9/11388889NA0002TF001005XXXXXXXPR0015" "G9/11388889NA0003TF001006XXXXXXXPR0016"
 [7] "G9/11388889NA0003TF001007XXXXXXXPR0017" "G9/11388889NA0003TF001008XXXXXXXPR0018"
 [9] "G9/11488898NA0004TF001009XXXXXXXPR0019" "G9/11488898NA0004TF001010XXXXXXXPR0010"
[11] "G9/11488898NA0004TF001011XXXXXXXPR0011" "G9/11488878NA0005TF001012XXXXXXXPR0011"
[13] "G9/11488878NA0005TF001013XXXXXXXPR0013" "G9/11488878NA0005TF001014XXXXXXXPR0011"
[15] "G9/11488878NA0006TF001015XXXXXXXPR0011" "G9/11488878NA0006TF001016XXXXXXXPR0011"

$G10
[1] "G10/CO113" "G10/RE113" "G10/WA113" "G10/WA114" "G10/FL114" "G10/IC114"

$G11
[1] "G11/CO11388888" "G11/CO11388889" "G11/RE11388889" "G11/WA11388889" "G11/WA11488898"
[6] "G11/FL11488898" "G11/FL11488878" "G11/IC11488878"

$<NA>
 [1] "G1/CO11388888NA0001" "G1/CO11388889NA0002" "G1/RE11388889NA0002" "G1/RE11388889NA0003"
 [5] "G1/WA11388889NA0003" "G1/WA11488898NA0004" "G1/FL11488898NA0004" "G1/FL11488878NA0005"
 [9] "G1/IC11488878NA0005" "G1/IC11488878NA0006"

$<NA>
 [1] "G2/CO11388888NA0001TF001001XXXXXXX" "G2/CO11388888NA0001TF001002XXXXXXX"
 [3] "G2/CO11388888NA0001TF001003XXXXXXX" "G2/CO11388889NA0002TF001004XXXXXXX"
 [5] "G2/RE11388889NA0002TF001005XXXXXXX" "G2/RE11388889NA0003TF001006XXXXXXX"
 [7] "G2/RE11388889NA0003TF001007XXXXXXX" "G2/WA11388889NA0003TF001008XXXXXXX"
 [9] "G2/WA11488898NA0004TF001009XXXXXXX" "G2/WA11488898NA0004TF001010XXXXXXX"
[11] "G2/FL11488898NA0004TF001011XXXXXXX" "G2/FL11488878NA0005TF001012XXXXXXX"
[13] "G2/FL11488878NA0005TF001013XXXXXXX" "G2/IC11488878NA0005TF001014XXXXXXX"
[15] "G2/IC11488878NA0006TF001015XXXXXXX" "G2/IC11488878NA0006TF001016XXXXXXX"

$<NA>
 [1] "G3/CO11388888NA0001TF001001XXXXXXXPR0011" "G3/CO11388888NA0001TF001002XXXXXXXPR0012"
 [3] "G3/CO11388888NA0001TF001003XXXXXXXPR0013" "G3/CO11388889NA0002TF001004XXXXXXXPR0014"
 [5] "G3/RE11388889NA0002TF001005XXXXXXXPR0015" "G3/RE11388889NA0003TF001006XXXXXXXPR0016"
 [7] "G3/RE11388889NA0003TF001007XXXXXXXPR0017" "G3/WA11388889NA0003TF001008XXXXXXXPR0018"
 [9] "G3/WA11488898NA0004TF001009XXXXXXXPR0019" "G3/WA11488898NA0004TF001010XXXXXXXPR0010"
[11] "G3/FL11488898NA0004TF001011XXXXXXXPR0011" "G3/FL11488878NA0005TF001012XXXXXXXPR0011"
[13] "G3/FL11488878NA0005TF001013XXXXXXXPR0013" "G3/IC11488878NA0005TF001014XXXXXXXPR0011"
[15] "G3/IC11488878NA0006TF001015XXXXXXXPR0011" "G3/IC11488878NA0006TF001016XXXXXXXPR0011"

$<NA>
 [1] "G4/CO0993113" "G4/CO0994113" "G4/RE0995113" "G4/RE0996113" "G4/WA0997113" "G4/WA0998114"
 [7] "G4/WA0999114" "G4/FL0983114" "G4/IC0973114" "G4/IC0972114"

$<NA>
 [1] "G5/CO099311388888" "G5/CO099411388888" "G5/CO099411388889" "G5/RE099511388889"
 [5] "G5/RE099611388889" "G5/WA099711388889" "G5/WA099811488898" "G5/WA099911488898"
 [9] "G5/FL098311488898" "G5/FL098311488878" "G5/IC097311488878" "G5/IC097211488878"

$<NA>
 [1] "G6/CO099311388888NA0001" "G6/CO099411388888NA0001" "G6/CO099411388889NA0002"
 [4] "G6/RE099511388889NA0002" "G6/RE099511388889NA0003" "G6/RE099611388889NA0003"
 [7] "G6/WA099711388889NA0003" "G6/WA099811488898NA0004" "G6/WA099911488898NA0004"
[10] "G6/FL098311488898NA0004" "G6/FL098311488878NA0005" "G6/IC097311488878NA0005"
[13] "G6/IC097211488878NA0006"

$<NA>
 [1] "G7/CO099311388888NA0001TF001001XXXXXXX" "G7/CO099311388888NA0001TF001002XXXXXXX"
 [3] "G7/CO099411388888NA0001TF001003XXXXXXX" "G7/CO099411388889NA0002TF001004XXXXXXX"
 [5] "G7/RE099511388889NA0002TF001005XXXXXXX" "G7/RE099511388889NA0003TF001006XXXXXXX"
 [7] "G7/RE099611388889NA0003TF001007XXXXXXX" "G7/WA099711388889NA0003TF001008XXXXXXX"
 [9] "G7/WA099811488898NA0004TF001009XXXXXXX" "G7/WA099911488898NA0004TF001010XXXXXXX"
[11] "G7/FL098311488898NA0004TF001011XXXXXXX" "G7/FL098311488878NA0005TF001012XXXXXXX"
[13] "G7/FL098311488878NA0005TF001013XXXXXXX" "G7/IC097311488878NA0005TF001014XXXXXXX"
[15] "G7/IC097211488878NA0006TF001015XXXXXXX" "G7/IC097211488878NA0006TF001016XXXXXXX"

$<NA>
 [1] "G8/CO099311388888NA0001TF001001XXXXXXXPR0011"
 [2] "G8/CO099311388888NA0001TF001002XXXXXXXPR0012"
 [3] "G8/CO099411388888NA0001TF001003XXXXXXXPR0013"
 [4] "G8/CO099411388889NA0002TF001004XXXXXXXPR0014"
 [5] "G8/RE099511388889NA0002TF001005XXXXXXXPR0015"
 [6] "G8/RE099511388889NA0003TF001006XXXXXXXPR0016"
 [7] "G8/RE099611388889NA0003TF001007XXXXXXXPR0017"
 [8] "G8/WA099711388889NA0003TF001008XXXXXXXPR0018"
 [9] "G8/WA099811488898NA0004TF001009XXXXXXXPR0019"
[10] "G8/WA099911488898NA0004TF001010XXXXXXXPR0010"
[11] "G8/FL098311488898NA0004TF001011XXXXXXXPR0011"
[12] "G8/FL098311488878NA0005TF001012XXXXXXXPR0011"
[13] "G8/FL098311488878NA0005TF001013XXXXXXXPR0013"
[14] "G8/IC097311488878NA0005TF001014XXXXXXXPR0011"
[15] "G8/IC097211488878NA0006TF001015XXXXXXXPR0011"
[16] "G8/IC097211488878NA0006TF001016XXXXXXXPR0011"

$<NA>
 [1] "G9/CO0993W01113" "G9/CO0994W02113" "G9/RE0995W01113" "G9/RE0995W02113" "G9/RE0996W03113"
 [6] "G9/WA0997W01113" "G9/WA0998W01114" "G9/WA0999W03114" "G9/FL0983W01114" "G9/FL0983W02114"
[11] "G9/IC0973W01114" "G9/IC0972W01114"

$<NA>
 [1] "G10/CO0993W0111388888" "G10/CO0994W0211388888" "G10/CO0994W0211388889"
 [4] "G10/RE0995W0111388889" "G10/RE0995W0211388889" "G10/RE0996W0311388889"
 [7] "G10/WA0997W0111388889" "G10/WA0998W0111488898" "G10/WA0999W0311488898"
[10] "G10/FL0983W0111488898" "G10/FL0983W0211488878" "G10/IC0973W0111488878"
[13] "G10/IC0972W0111488878"

$<NA>
 [1] "G11/CO0993W0111388888NA0001" "G11/CO0994W0211388888NA0001" "G11/CO0994W0211388889NA0002"
 [4] "G11/RE0995W0111388889NA0002" "G11/RE0995W0211388889NA0003" "G11/RE0996W0311388889NA0003"
 [7] "G11/WA0997W0111388889NA0003" "G11/WA0998W0111488898NA0004" "G11/WA0999W0311488898NA0004"
[10] "G11/FL0983W0111488898NA0004" "G11/FL0983W0211488878NA0005" "G11/IC0973W0111488878NA0005"
[13] "G11/IC0972W0111488878NA0006"

$<NA>
 [1] "G1/CO0993W0111388888NA0001TF001001XXXXXXX" "G1/CO0993W0111388888NA0001TF001002XXXXXXX"
 [3] "G1/CO0994W0211388888NA0001TF001003XXXXXXX" "G1/CO0994W0211388889NA0002TF001004XXXXXXX"
 [5] "G1/RE0995W0111388889NA0002TF001005XXXXXXX" "G1/RE0995W0211388889NA0003TF001006XXXXXXX"
 [7] "G1/RE0996W0311388889NA0003TF001007XXXXXXX" "G1/WA0997W0111388889NA0003TF001008XXXXXXX"
 [9] "G1/WA0998W0111488898NA0004TF001009XXXXXXX" "G1/WA0999W0311488898NA0004TF001010XXXXXXX"
[11] "G1/FL0983W0111488898NA0004TF001011XXXXXXX" "G1/FL0983W0211488878NA0005TF001012XXXXXXX"
[13] "G1/FL0983W0211488878NA0005TF001013XXXXXXX" "G1/IC0973W0111488878NA0005TF001014XXXXXXX"
[15] "G1/IC0972W0111488878NA0006TF001015XXXXXXX" "G1/IC0972W0111488878NA0006TF001016XXXXXXX"

$<NA>
 [1] "G2/CO0993W0111388888NA0001TF001001XXXXXXXPR0011"
 [2] "G2/CO0993W0111388888NA0001TF001002XXXXXXXPR0012"
 [3] "G2/CO0994W0211388888NA0001TF001003XXXXXXXPR0013"
 [4] "G2/CO0994W0211388889NA0002TF001004XXXXXXXPR0014"
 [5] "G2/RE0995W0111388889NA0002TF001005XXXXXXXPR0015"
 [6] "G2/RE0995W0211388889NA0003TF001006XXXXXXXPR0016"
 [7] "G2/RE0996W0311388889NA0003TF001007XXXXXXXPR0017"
 [8] "G2/WA0997W0111388889NA0003TF001008XXXXXXXPR0018"
 [9] "G2/WA0998W0111488898NA0004TF001009XXXXXXXPR0019"
[10] "G2/WA0999W0311488898NA0004TF001010XXXXXXXPR0010"
[11] "G2/FL0983W0111488898NA0004TF001011XXXXXXXPR0011"
[12] "G2/FL0983W0211488878NA0005TF001012XXXXXXXPR0011"
[13] "G2/FL0983W0211488878NA0005TF001013XXXXXXXPR0013"
[14] "G2/IC0973W0111488878NA0005TF001014XXXXXXXPR0011"
[15] "G2/IC0972W0111488878NA0006TF001015XXXXXXXPR0011"
[16] "G2/IC0972W0111488878NA0006TF001016XXXXXXXPR0011"

$<NA>
 [1] "G3/CO0993W010100113" "G3/CO0993W011100113" "G3/CO0994W022100113" "G3/CO0994W023100113"
 [5] "G3/RE0995W014100113" "G3/RE0995W025100113" "G3/RE0996W031100113" "G3/WA0997W012100113"
 [9] "G3/WA0998W013100114" "G3/WA0999W034100114" "G3/FL0983W015100114" "G3/FL0983W021100114"
[13] "G3/FL0983W022100114" "G3/IC0973W013100114" "G3/IC0972W014100114" "G3/IC0972W015100114"

$<NA>
 [1] "G4/CO0993W01010011388888" "G4/CO0993W01110011388888" "G4/CO0994W02210011388888"
 [4] "G4/CO0994W02310011388889" "G4/RE0995W01410011388889" "G4/RE0995W02510011388889"
 [7] "G4/RE0996W03110011388889" "G4/WA0997W01210011388889" "G4/WA0998W01310011488898"
[10] "G4/WA0999W03410011488898" "G4/FL0983W01510011488898" "G4/FL0983W02110011488878"
[13] "G4/FL0983W02210011488878" "G4/IC0973W01310011488878" "G4/IC0972W01410011488878"
[16] "G4/IC0972W01510011488878"

$<NA>
 [1] "G5/CO0993W01010011388888NA0001" "G5/CO0993W01110011388888NA0001"
 [3] "G5/CO0994W02210011388888NA0001" "G5/CO0994W02310011388889NA0002"
 [5] "G5/RE0995W01410011388889NA0002" "G5/RE0995W02510011388889NA0003"
 [7] "G5/RE0996W03110011388889NA0003" "G5/WA0997W01210011388889NA0003"
 [9] "G5/WA0998W01310011488898NA0004" "G5/WA0999W03410011488898NA0004"
[11] "G5/FL0983W01510011488898NA0004" "G5/FL0983W02110011488878NA0005"
[13] "G5/FL0983W02210011488878NA0005" "G5/IC0973W01310011488878NA0005"
[15] "G5/IC0972W01410011488878NA0006" "G5/IC0972W01510011488878NA0006"

$<NA>
 [1] "G6/CO0993W01010011388888NA0001TF001001XXXXXXX"
 [2] "G6/CO0993W01110011388888NA0001TF001002XXXXXXX"
 [3] "G6/CO0994W02210011388888NA0001TF001003XXXXXXX"
 [4] "G6/CO0994W02310011388889NA0002TF001004XXXXXXX"
 [5] "G6/RE0995W01410011388889NA0002TF001005XXXXXXX"
 [6] "G6/RE0995W02510011388889NA0003TF001006XXXXXXX"
 [7] "G6/RE0996W03110011388889NA0003TF001007XXXXXXX"
 [8] "G6/WA0997W01210011388889NA0003TF001008XXXXXXX"
 [9] "G6/WA0998W01310011488898NA0004TF001009XXXXXXX"
[10] "G6/WA0999W03410011488898NA0004TF001010XXXXXXX"
[11] "G6/FL0983W01510011488898NA0004TF001011XXXXXXX"
[12] "G6/FL0983W02110011488878NA0005TF001012XXXXXXX"
[13] "G6/FL0983W02210011488878NA0005TF001013XXXXXXX"
[14] "G6/IC0973W01310011488878NA0005TF001014XXXXXXX"
[15] "G6/IC0972W01410011488878NA0006TF001015XXXXXXX"
[16] "G6/IC0972W01510011488878NA0006TF001016XXXXXXX"

if the line in charge of duplicates in GmatrixG is removed

GmatrixG <- function(xmat) {
  if (is.character(xmat)) {
    # Convert character to integer
    gmat <- t(apply(xmat, 1, function(x) as.integer(factor(x, unique(x)))))
  } else {
    gmat  <- xmat
  }
  # Insert the first & last rows
  nc.xmat <- ncol(xmat)
  gmat <- rbind(rep(1L, nc.xmat), gmat, seq(1L, nc.xmat))
  #Remove line in charge of duplicates
  #gmat <- gmat[!duplicated(gmat), , drop = FALSE] # Remove possible duplicated
  return(structure(gmat, class = "gmatrix"))
}

Then the groups are generated as expected

$G1
[1] "G1/CO" "G1/RE" "G1/WA" "G1/FL" "G1/IC"

$G2
 [1] "G2/CO0993" "G2/CO0994" "G2/RE0995" "G2/RE0996" "G2/WA0997" "G2/WA0998" "G2/WA0999"
 [8] "G2/FL0983" "G2/IC0973" "G2/IC0972"

$G3
 [1] "G3/CO0993W01" "G3/CO0994W02" "G3/RE0995W01" "G3/RE0995W02" "G3/RE0996W03" "G3/WA0997W01"
 [7] "G3/WA0998W01" "G3/WA0999W03" "G3/FL0983W01" "G3/FL0983W02" "G3/IC0973W01" "G3/IC0972W01"

$G4
 [1] "G4/CO0993W010100" "G4/CO0993W011100" "G4/CO0994W022100" "G4/CO0994W023100"
 [5] "G4/RE0995W014100" "G4/RE0995W025100" "G4/RE0996W031100" "G4/WA0997W012100"
 [9] "G4/WA0998W013100" "G4/WA0999W034100" "G4/FL0983W015100" "G4/FL0983W021100"
[13] "G4/FL0983W022100" "G4/IC0973W013100" "G4/IC0972W014100" "G4/IC0972W015100"

$G5
[1] "G5/113" "G5/114"

$G6
[1] "G6/11388888" "G6/11388889" "G6/11488898" "G6/11488878"

$G7
[1] "G7/11388888NA0001" "G7/11388889NA0002" "G7/11388889NA0003" "G7/11488898NA0004"
[5] "G7/11488878NA0005" "G7/11488878NA0006"

$G8
 [1] "G8/11388888NA0001TF001001XXXXXXX" "G8/11388888NA0001TF001002XXXXXXX"
 [3] "G8/11388888NA0001TF001003XXXXXXX" "G8/11388889NA0002TF001004XXXXXXX"
 [5] "G8/11388889NA0002TF001005XXXXXXX" "G8/11388889NA0003TF001006XXXXXXX"
 [7] "G8/11388889NA0003TF001007XXXXXXX" "G8/11388889NA0003TF001008XXXXXXX"
 [9] "G8/11488898NA0004TF001009XXXXXXX" "G8/11488898NA0004TF001010XXXXXXX"
[11] "G8/11488898NA0004TF001011XXXXXXX" "G8/11488878NA0005TF001012XXXXXXX"
[13] "G8/11488878NA0005TF001013XXXXXXX" "G8/11488878NA0005TF001014XXXXXXX"
[15] "G8/11488878NA0006TF001015XXXXXXX" "G8/11488878NA0006TF001016XXXXXXX"

$G9
 [1] "G9/11388888NA0001TF001001XXXXXXXPR0011" "G9/11388888NA0001TF001002XXXXXXXPR0012"
 [3] "G9/11388888NA0001TF001003XXXXXXXPR0013" "G9/11388889NA0002TF001004XXXXXXXPR0014"
 [5] "G9/11388889NA0002TF001005XXXXXXXPR0015" "G9/11388889NA0003TF001006XXXXXXXPR0016"
 [7] "G9/11388889NA0003TF001007XXXXXXXPR0017" "G9/11388889NA0003TF001008XXXXXXXPR0018"
 [9] "G9/11488898NA0004TF001009XXXXXXXPR0019" "G9/11488898NA0004TF001010XXXXXXXPR0010"
[11] "G9/11488898NA0004TF001011XXXXXXXPR0011" "G9/11488878NA0005TF001012XXXXXXXPR0011"
[13] "G9/11488878NA0005TF001013XXXXXXXPR0013" "G9/11488878NA0005TF001014XXXXXXXPR0011"
[15] "G9/11488878NA0006TF001015XXXXXXXPR0011" "G9/11488878NA0006TF001016XXXXXXXPR0011"

$G10
[1] "G10/CO113" "G10/RE113" "G10/WA113" "G10/WA114" "G10/FL114" "G10/IC114"

$G11
[1] "G11/CO11388888" "G11/CO11388889" "G11/RE11388889" "G11/WA11388889" "G11/WA11488898"
[6] "G11/FL11488898" "G11/FL11488878" "G11/IC11488878"

$G12
 [1] "G12/CO11388888NA0001" "G12/CO11388889NA0002" "G12/RE11388889NA0002"
 [4] "G12/RE11388889NA0003" "G12/WA11388889NA0003" "G12/WA11488898NA0004"
 [7] "G12/FL11488898NA0004" "G12/FL11488878NA0005" "G12/IC11488878NA0005"
[10] "G12/IC11488878NA0006"

$G13
 [1] "G13/CO11388888NA0001TF001001XXXXXXX" "G13/CO11388888NA0001TF001002XXXXXXX"
 [3] "G13/CO11388888NA0001TF001003XXXXXXX" "G13/CO11388889NA0002TF001004XXXXXXX"
 [5] "G13/RE11388889NA0002TF001005XXXXXXX" "G13/RE11388889NA0003TF001006XXXXXXX"
 [7] "G13/RE11388889NA0003TF001007XXXXXXX" "G13/WA11388889NA0003TF001008XXXXXXX"
 [9] "G13/WA11488898NA0004TF001009XXXXXXX" "G13/WA11488898NA0004TF001010XXXXXXX"
[11] "G13/FL11488898NA0004TF001011XXXXXXX" "G13/FL11488878NA0005TF001012XXXXXXX"
[13] "G13/FL11488878NA0005TF001013XXXXXXX" "G13/IC11488878NA0005TF001014XXXXXXX"
[15] "G13/IC11488878NA0006TF001015XXXXXXX" "G13/IC11488878NA0006TF001016XXXXXXX"

$G14
 [1] "G14/CO11388888NA0001TF001001XXXXXXXPR0011" "G14/CO11388888NA0001TF001002XXXXXXXPR0012"
 [3] "G14/CO11388888NA0001TF001003XXXXXXXPR0013" "G14/CO11388889NA0002TF001004XXXXXXXPR0014"
 [5] "G14/RE11388889NA0002TF001005XXXXXXXPR0015" "G14/RE11388889NA0003TF001006XXXXXXXPR0016"
 [7] "G14/RE11388889NA0003TF001007XXXXXXXPR0017" "G14/WA11388889NA0003TF001008XXXXXXXPR0018"
 [9] "G14/WA11488898NA0004TF001009XXXXXXXPR0019" "G14/WA11488898NA0004TF001010XXXXXXXPR0010"
[11] "G14/FL11488898NA0004TF001011XXXXXXXPR0011" "G14/FL11488878NA0005TF001012XXXXXXXPR0011"
[13] "G14/FL11488878NA0005TF001013XXXXXXXPR0013" "G14/IC11488878NA0005TF001014XXXXXXXPR0011"
[15] "G14/IC11488878NA0006TF001015XXXXXXXPR0011" "G14/IC11488878NA0006TF001016XXXXXXXPR0011"

$G15
 [1] "G15/CO0993113" "G15/CO0994113" "G15/RE0995113" "G15/RE0996113" "G15/WA0997113"
 [6] "G15/WA0998114" "G15/WA0999114" "G15/FL0983114" "G15/IC0973114" "G15/IC0972114"

$G16
 [1] "G16/CO099311388888" "G16/CO099411388888" "G16/CO099411388889" "G16/RE099511388889"
 [5] "G16/RE099611388889" "G16/WA099711388889" "G16/WA099811488898" "G16/WA099911488898"
 [9] "G16/FL098311488898" "G16/FL098311488878" "G16/IC097311488878" "G16/IC097211488878"

$G17
 [1] "G17/CO099311388888NA0001" "G17/CO099411388888NA0001" "G17/CO099411388889NA0002"
 [4] "G17/RE099511388889NA0002" "G17/RE099511388889NA0003" "G17/RE099611388889NA0003"
 [7] "G17/WA099711388889NA0003" "G17/WA099811488898NA0004" "G17/WA099911488898NA0004"
[10] "G17/FL098311488898NA0004" "G17/FL098311488878NA0005" "G17/IC097311488878NA0005"
[13] "G17/IC097211488878NA0006"

$G18
 [1] "G18/CO099311388888NA0001TF001001XXXXXXX" "G18/CO099311388888NA0001TF001002XXXXXXX"
 [3] "G18/CO099411388888NA0001TF001003XXXXXXX" "G18/CO099411388889NA0002TF001004XXXXXXX"
 [5] "G18/RE099511388889NA0002TF001005XXXXXXX" "G18/RE099511388889NA0003TF001006XXXXXXX"
 [7] "G18/RE099611388889NA0003TF001007XXXXXXX" "G18/WA099711388889NA0003TF001008XXXXXXX"
 [9] "G18/WA099811488898NA0004TF001009XXXXXXX" "G18/WA099911488898NA0004TF001010XXXXXXX"
[11] "G18/FL098311488898NA0004TF001011XXXXXXX" "G18/FL098311488878NA0005TF001012XXXXXXX"
[13] "G18/FL098311488878NA0005TF001013XXXXXXX" "G18/IC097311488878NA0005TF001014XXXXXXX"
[15] "G18/IC097211488878NA0006TF001015XXXXXXX" "G18/IC097211488878NA0006TF001016XXXXXXX"

$G19
 [1] "G19/CO099311388888NA0001TF001001XXXXXXXPR0011"
 [2] "G19/CO099311388888NA0001TF001002XXXXXXXPR0012"
 [3] "G19/CO099411388888NA0001TF001003XXXXXXXPR0013"
 [4] "G19/CO099411388889NA0002TF001004XXXXXXXPR0014"
 [5] "G19/RE099511388889NA0002TF001005XXXXXXXPR0015"
 [6] "G19/RE099511388889NA0003TF001006XXXXXXXPR0016"
 [7] "G19/RE099611388889NA0003TF001007XXXXXXXPR0017"
 [8] "G19/WA099711388889NA0003TF001008XXXXXXXPR0018"
 [9] "G19/WA099811488898NA0004TF001009XXXXXXXPR0019"
[10] "G19/WA099911488898NA0004TF001010XXXXXXXPR0010"
[11] "G19/FL098311488898NA0004TF001011XXXXXXXPR0011"
[12] "G19/FL098311488878NA0005TF001012XXXXXXXPR0011"
[13] "G19/FL098311488878NA0005TF001013XXXXXXXPR0013"
[14] "G19/IC097311488878NA0005TF001014XXXXXXXPR0011"
[15] "G19/IC097211488878NA0006TF001015XXXXXXXPR0011"
[16] "G19/IC097211488878NA0006TF001016XXXXXXXPR0011"

$G20
 [1] "G20/CO0993W01113" "G20/CO0994W02113" "G20/RE0995W01113" "G20/RE0995W02113"
 [5] "G20/RE0996W03113" "G20/WA0997W01113" "G20/WA0998W01114" "G20/WA0999W03114"
 [9] "G20/FL0983W01114" "G20/FL0983W02114" "G20/IC0973W01114" "G20/IC0972W01114"

$G21
 [1] "G21/CO0993W0111388888" "G21/CO0994W0211388888" "G21/CO0994W0211388889"
 [4] "G21/RE0995W0111388889" "G21/RE0995W0211388889" "G21/RE0996W0311388889"
 [7] "G21/WA0997W0111388889" "G21/WA0998W0111488898" "G21/WA0999W0311488898"
[10] "G21/FL0983W0111488898" "G21/FL0983W0211488878" "G21/IC0973W0111488878"
[13] "G21/IC0972W0111488878"

$G22
 [1] "G22/CO0993W0111388888NA0001" "G22/CO0994W0211388888NA0001" "G22/CO0994W0211388889NA0002"
 [4] "G22/RE0995W0111388889NA0002" "G22/RE0995W0211388889NA0003" "G22/RE0996W0311388889NA0003"
 [7] "G22/WA0997W0111388889NA0003" "G22/WA0998W0111488898NA0004" "G22/WA0999W0311488898NA0004"
[10] "G22/FL0983W0111488898NA0004" "G22/FL0983W0211488878NA0005" "G22/IC0973W0111488878NA0005"
[13] "G22/IC0972W0111488878NA0006"

$G23
 [1] "G23/CO0993W0111388888NA0001TF001001XXXXXXX" "G23/CO0993W0111388888NA0001TF001002XXXXXXX"
 [3] "G23/CO0994W0211388888NA0001TF001003XXXXXXX" "G23/CO0994W0211388889NA0002TF001004XXXXXXX"
 [5] "G23/RE0995W0111388889NA0002TF001005XXXXXXX" "G23/RE0995W0211388889NA0003TF001006XXXXXXX"
 [7] "G23/RE0996W0311388889NA0003TF001007XXXXXXX" "G23/WA0997W0111388889NA0003TF001008XXXXXXX"
 [9] "G23/WA0998W0111488898NA0004TF001009XXXXXXX" "G23/WA0999W0311488898NA0004TF001010XXXXXXX"
[11] "G23/FL0983W0111488898NA0004TF001011XXXXXXX" "G23/FL0983W0211488878NA0005TF001012XXXXXXX"
[13] "G23/FL0983W0211488878NA0005TF001013XXXXXXX" "G23/IC0973W0111488878NA0005TF001014XXXXXXX"
[15] "G23/IC0972W0111488878NA0006TF001015XXXXXXX" "G23/IC0972W0111488878NA0006TF001016XXXXXXX"

$G24
 [1] "G24/CO0993W0111388888NA0001TF001001XXXXXXXPR0011"
 [2] "G24/CO0993W0111388888NA0001TF001002XXXXXXXPR0012"
 [3] "G24/CO0994W0211388888NA0001TF001003XXXXXXXPR0013"
 [4] "G24/CO0994W0211388889NA0002TF001004XXXXXXXPR0014"
 [5] "G24/RE0995W0111388889NA0002TF001005XXXXXXXPR0015"
 [6] "G24/RE0995W0211388889NA0003TF001006XXXXXXXPR0016"
 [7] "G24/RE0996W0311388889NA0003TF001007XXXXXXXPR0017"
 [8] "G24/WA0997W0111388889NA0003TF001008XXXXXXXPR0018"
 [9] "G24/WA0998W0111488898NA0004TF001009XXXXXXXPR0019"
[10] "G24/WA0999W0311488898NA0004TF001010XXXXXXXPR0010"
[11] "G24/FL0983W0111488898NA0004TF001011XXXXXXXPR0011"
[12] "G24/FL0983W0211488878NA0005TF001012XXXXXXXPR0011"
[13] "G24/FL0983W0211488878NA0005TF001013XXXXXXXPR0013"
[14] "G24/IC0973W0111488878NA0005TF001014XXXXXXXPR0011"
[15] "G24/IC0972W0111488878NA0006TF001015XXXXXXXPR0011"
[16] "G24/IC0972W0111488878NA0006TF001016XXXXXXXPR0011"

$G25
 [1] "G25/CO0993W010100113" "G25/CO0993W011100113" "G25/CO0994W022100113"
 [4] "G25/CO0994W023100113" "G25/RE0995W014100113" "G25/RE0995W025100113"
 [7] "G25/RE0996W031100113" "G25/WA0997W012100113" "G25/WA0998W013100114"
[10] "G25/WA0999W034100114" "G25/FL0983W015100114" "G25/FL0983W021100114"
[13] "G25/FL0983W022100114" "G25/IC0973W013100114" "G25/IC0972W014100114"
[16] "G25/IC0972W015100114"

$G26
 [1] "G26/CO0993W01010011388888" "G26/CO0993W01110011388888" "G26/CO0994W02210011388888"
 [4] "G26/CO0994W02310011388889" "G26/RE0995W01410011388889" "G26/RE0995W02510011388889"
 [7] "G26/RE0996W03110011388889" "G26/WA0997W01210011388889" "G26/WA0998W01310011488898"
[10] "G26/WA0999W03410011488898" "G26/FL0983W01510011488898" "G26/FL0983W02110011488878"
[13] "G26/FL0983W02210011488878" "G26/IC0973W01310011488878" "G26/IC0972W01410011488878"
[16] "G26/IC0972W01510011488878"

$G27
 [1] "G27/CO0993W01010011388888NA0001" "G27/CO0993W01110011388888NA0001"
 [3] "G27/CO0994W02210011388888NA0001" "G27/CO0994W02310011388889NA0002"
 [5] "G27/RE0995W01410011388889NA0002" "G27/RE0995W02510011388889NA0003"
 [7] "G27/RE0996W03110011388889NA0003" "G27/WA0997W01210011388889NA0003"
 [9] "G27/WA0998W01310011488898NA0004" "G27/WA0999W03410011488898NA0004"
[11] "G27/FL0983W01510011488898NA0004" "G27/FL0983W02110011488878NA0005"
[13] "G27/FL0983W02210011488878NA0005" "G27/IC0973W01310011488878NA0005"
[15] "G27/IC0972W01410011488878NA0006" "G27/IC0972W01510011488878NA0006"

$G28
 [1] "G28/CO0993W01010011388888NA0001TF001001XXXXXXX"
 [2] "G28/CO0993W01110011388888NA0001TF001002XXXXXXX"
 [3] "G28/CO0994W02210011388888NA0001TF001003XXXXXXX"
 [4] "G28/CO0994W02310011388889NA0002TF001004XXXXXXX"
 [5] "G28/RE0995W01410011388889NA0002TF001005XXXXXXX"
 [6] "G28/RE0995W02510011388889NA0003TF001006XXXXXXX"
 [7] "G28/RE0996W03110011388889NA0003TF001007XXXXXXX"
 [8] "G28/WA0997W01210011388889NA0003TF001008XXXXXXX"
 [9] "G28/WA0998W01310011488898NA0004TF001009XXXXXXX"
[10] "G28/WA0999W03410011488898NA0004TF001010XXXXXXX"
[11] "G28/FL0983W01510011488898NA0004TF001011XXXXXXX"
[12] "G28/FL0983W02110011488878NA0005TF001012XXXXXXX"
[13] "G28/FL0983W02210011488878NA0005TF001013XXXXXXX"
[14] "G28/IC0973W01310011488878NA0005TF001014XXXXXXX"
[15] "G28/IC0972W01410011488878NA0006TF001015XXXXXXX"
[16] "G28/IC0972W01510011488878NA0006TF001016XXXXXXX"

Ps: Sorry for the large example.

method and weights argument in forecast.gts()

The options here are confusing, and the default should not be MinT as it is much slower than WLS without giving much better accuracy.

I suggest we use

  method = c( "comb", "bu", "mo",  "tdgsa", "tdgsf", "tdfp")

-- the same as in the current CRAN version -- and

  weights = c("wls", "ols", "mint", "nseries")

We could allow "none" and "sd" to be equivalent to "ols" and "wls" respectively, for backwards compatibility.

About how to generate a set of revised forecasts

I already generated independent base forecast for each series in the hierarchy, As these base forecasts are independently generated they will not be aggregate consistent" (i.e.,
they will not add up according to the hierarchical structure).I just want to generates a
set of revised forecasts that would aggregate consistently with the hierarchical structure. Can use your funcitons?

`tdfp` gives a warning when `h = 1`

library(hts)
#> Loading required package: forecast
#> Loading required package: SparseM
#> 
#> Attaching package: 'SparseM'
#> The following object is masked from 'package:base':
#> 
#>     backsolve
#> Loading required package: Matrix
#> Loading required package: matrixcalc
forecast(htseg1, fmethod = "arima", method = "tdfp", h = 1)
#> Warning in flist[[2L]]/new.flist[[1L]]: Recycling array of length 1 in vector-array arithmetic is deprecated.
#>   Use c() or as.vector() instead.
#> Hierarchical Time Series 
#> 3 Levels 
#> Number of nodes at each level: 1 2 5 
#> Total number of series: 8 
#> Number of observations in each historical series: 10 
#> Number of forecasts per series: 1 
#> Top level series of forecasts: 
#> Time Series:
#> Start = 2002 
#> End = 2002 
#> Frequency = 1 
#> [1] 53.71128

Improve the documentation of algorithms in combinef and forecast.gts

Need to explain c("lu", "cg", "chol", "recursive", "slm"),

Can we use forecast.gts function and include forecasts from a state space model

each and every time series in grouped time series are forecasted separately in forecast.gts function. If I have forecasts from a different method (State space method) for both top and bottom levels, can I have them included in the forecast.gts function?

hts() does not preserve frequency of bottom level time series

# sessionInfo()
# hts_4.5
# forecast_6.1
nodes <- list(2, c(3, 2))
abc <- ts(5 + matrix(sort(rnorm(500)), ncol = 5, nrow = 100), frequency = 7)
frequency(abc) # 7
frequency(hts(abc, nodes)) # 1

My intention is that after I create a hts object, I'd like to obtain the first K fourier terms of each underlying time series.

When I call fourier(hts(abc, nodes), K = 5), I am thrown an exception - "K must not be greater than period/2" because frequency(hts(abc, nodes)) is 1.

Issue with gts

Hello,
I suspect a bug in the forecast when using a gts object. It attached zip file contains the R code in txt format and the data file in csv format. The error thrown by the forecast command is:
Error in x[, (1 + cs[i]):cs[i + 1]] <- xx :
number of items to replace is not a multiple of replacement length
I have been unsuccessful finding assistance with this online and noticed that if I permute the second column of the time series matrix, all works fine but not if the first column is permuted. Thus, I thought there may be an issue with the interaction between the forcast command and the gts object.
Respecfully,
Brad Gubser
[email protected]

Issue_in_GTS_forecast.zip

Forecast horizon ignored in stlf forecast

When forecasts are made using the stlf method (one of the methods recommended by the author), the input forecast horizon is ignored and instead the default horizon of 2L * frequency(object$bts) is used. The problem can be reproduced using the code below.

nodes <- list(2, c(3, 2))
abc <- ts(5 + matrix(sort(rnorm(500)), ncol = 5, nrow = 100),frequency=4)
x <- hts(abc, nodes)
forecast(x, h = 4, method = "comb", algorithms = "lu",FUN = stlf)

Thank you!

MinT needs covariance matrix to be positive definite.

Is there a way to enforce positive definite?

I also ran earlier into the same problem as #42 so I rounded both the forecasts and the residuals and replaced any 0s with 1s.

forecast.gts silently produces NAN for all levels when at least one base level is all zeros

hts version is 4.4 from CRAN
See the reproducible example below

library(hts)
myhts <- hts(cbind(wineind, 0))

# The total is fine here
aggts(myhts)

# The forecast produces NAN at all levels, 
# including Total and wineind
forecast.gts(myhts)

Warning message if constant columns are passed to MinT

Would be great to implement simple warning message when constant columns are passed to MinT that leads to error "Error in if (!is.symmetric.matrix(x)) stop("argument x is not a symmetric matrix")".
Maybe it's even worth adding an option to add small random noise to such columns.

#43 #42

MinT: Error in if (!is.symmetric.matrix(x)) stop("argument x is not a symmetric matrix") : missing value where TRUE/FALSE needed

Hello, when i use MinT function，input 7 days(2018.10.22-28) forecasts and 7 days(2018.10.15-21) residual, but I got the error:

Error in if (!is.symmetric.matrix(x)) stop("argument x is not a symmetric matrix") :
missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In cov2cor(covm) :
diag(.) had 0 or NA entries; non-finite result is doubtful
2: In cov2cor(tar) :
diag(.) had 0 or NA entries; non-finite result is doubtful

How to solve this error? I looking forward to hearing from you

earowang / hts Goto Github PK

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