Point Forecast Reconciliation - Provides classical (bottom-up), optimal and heuristic combination forecast reconciliation procedures for cross-sectional, temporal, and cross-temporal linearly constrained time series.
Home Page: https://danigiro.github.io/FoReco/index.html
License: GNU General Public License v3.0
Prepare for release:
git pulldevtools::build_readme()urlchecker::url_check()devtools::check(remote = TRUE, manual = TRUE)devtools::check_win_devel()rhub::check_for_cran()revdepcheck::revdep_check(num_workers = 4)cran-comments.mdgit pushSubmit to CRAN:
usethis::use_version('patch')devtools::submit_cran()Wait for CRAN...
git pushusethis::use_github_release()usethis::use_dev_version()git pushPrepare for release:
git pulldevtools::build_readme()urlchecker::url_check()devtools::check(remote = TRUE, manual = TRUE)devtools::check_win_devel()rhub::check_for_cran()revdepcheck::revdep_check(num_workers = 4)cran-comments.mdgit pushSubmit to CRAN:
usethis::use_version('patch')devtools::submit_cran()Wait for CRAN...
git pushusethis::use_github_release()usethis::use_dev_version()git push@danigiro I wanted to convert this package to Python and use it in produciton. But I am not able to find alternatives to the some parts of code. Any info on what will be best way to convert it.. where do I start..
Please see below.
library(hts)
#> Loading required package: forecast
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
library(FoReco)
#> Warning: package 'FoReco' was built under R version 4.3.3
#> Loading required package: Matrix
#> Loading required package: osqp
#> Warning: package 'osqp' was built under R version 4.3.3
#> ──────────────────────────────────────────────────────────────── FoReco 0.2.6 ──
set.seed(123)
abc <- ts(0.5 + matrix(sort(rnorm(200)), ncol = 4, nrow = 50))
nodes <- list(2, c(2, 2))
y <- hts(abc, nodes = nodes)
#> Since argument characters are not specified, the default labelling system is used.
h <- 12
ally <- aggts(y)
n <- nrow(ally)
p <- ncol(ally)
allf <- matrix(NA,nrow = h,ncol = ncol(ally))
for(i in 1:p)
{
fit <- auto.arima(ally[, i])
allf[, i] <- forecast(fit, h = h)$mean
}
htsrec(allf, comb = "ols", C = smatrix(y)[1:3,], keep = "recf", nn = FALSE)
#> serie1 serie2 serie3 serie4 serie5 serie6 serie7
#> h1 5.294030 0.28844210 5.005588 -0.1386732 0.4271153 1.0532083 3.952380
#> h2 5.473805 0.26662846 5.207177 -0.1490871 0.4157156 1.0434417 4.163735
#> h3 5.653610 0.24486500 5.408745 -0.1597520 0.4046170 1.0336652 4.375080
#> h4 5.833394 0.22306733 5.610327 -0.1702386 0.3933059 1.0238954 4.586432
#> h5 6.013193 0.20129296 5.811900 -0.1808519 0.3821448 1.0141210 4.797779
#> h6 6.192981 0.17950274 6.013479 -0.1913751 0.3708778 1.0043498 5.009129
#> h7 6.372777 0.15772329 6.215053 -0.2019623 0.3596856 0.9945764 5.220477
#> h8 6.552567 0.13593653 6.416631 -0.2125041 0.3484406 0.9848045 5.431826
#> h9 6.732361 0.11415473 6.618206 -0.2230781 0.3372329 0.9750316 5.643175
#> h10 6.912153 0.09236957 6.819783 -0.2336292 0.3259988 0.9652594 5.854524
#> h11 7.091946 0.07058668 7.021359 -0.2441967 0.3147833 0.9554867 6.065873
#> h12 7.271738 0.04880226 7.222936 -0.2547525 0.3035547 0.9457143 6.277222
combinef(allf, nodes = nodes, nonnegative = FALSE, keep = "all")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 5.294030 0.28844210 5.005588 -0.1386732 0.4271153 1.0532083 3.952380
#> [2,] 5.473805 0.26662846 5.207177 -0.1490871 0.4157156 1.0434417 4.163735
#> [3,] 5.653610 0.24486500 5.408745 -0.1597520 0.4046170 1.0336652 4.375080
#> [4,] 5.833394 0.22306733 5.610327 -0.1702386 0.3933059 1.0238954 4.586432
#> [5,] 6.013193 0.20129296 5.811900 -0.1808519 0.3821448 1.0141210 4.797779
#> [6,] 6.192981 0.17950274 6.013479 -0.1913751 0.3708778 1.0043498 5.009129
#> [7,] 6.372777 0.15772329 6.215053 -0.2019623 0.3596856 0.9945764 5.220477
#> [8,] 6.552567 0.13593653 6.416631 -0.2125041 0.3484406 0.9848045 5.431826
#> [9,] 6.732361 0.11415473 6.618206 -0.2230781 0.3372329 0.9750316 5.643175
#> [10,] 6.912153 0.09236957 6.819783 -0.2336292 0.3259988 0.9652594 5.854524
#> [11,] 7.091946 0.07058668 7.021359 -0.2441967 0.3147833 0.9554867 6.065873
#> [12,] 7.271738 0.04880226 7.222936 -0.2547525 0.3035547 0.9457143 6.277222
htsrec(allf, comb = "ols", C = smatrix(y)[1:3,], keep = "recf", nn = TRUE)
#> Warning: OSQP flag -4 OSQP pri_res 1.89503316505579e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89476372725039e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89535603567492e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89519846571784e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89522438631684e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89534762284893e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89524621418968e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.8955886389449e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89518414117629e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89527509064646e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89532261174463e-06
#> Warning: OSQP flag -4 OSQP pri_res 1.89510910786339e-06
#> $recf
#> serie1 serie2 serie3 serie4 serie5 serie6
#> h1 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h2 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h3 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h4 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h5 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h6 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h7 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h8 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h9 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h10 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h11 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> h12 2143289344 2143289344 2143289344 2143289344 2143289344 2143289344
#> serie7
#> h1 2143289344
#> h2 2143289344
#> h3 2143289344
#> h4 2143289344
#> h5 2143289344
#> h6 2143289344
#> h7 2143289344
#> h8 2143289344
#> h9 2143289344
#> h10 2143289344
#> h11 2143289344
#> h12 2143289344
#>
#> $info
#> obj_val run_time iter pri_res status status_polish
#> 1 -1e+30 0.0000747 25 1.895033e-06 -4 0
#> 2 -1e+30 0.0000504 25 1.894764e-06 -4 0
#> 3 -1e+30 0.0000567 25 1.895356e-06 -4 0
#> 4 -1e+30 0.0000459 25 1.895198e-06 -4 0
#> 5 -1e+30 0.0000467 25 1.895224e-06 -4 0
#> 6 -1e+30 0.0000534 25 1.895348e-06 -4 0
#> 7 -1e+30 0.0000527 25 1.895246e-06 -4 0
#> 8 -1e+30 0.0000487 25 1.895589e-06 -4 0
#> 9 -1e+30 0.0000731 25 1.895184e-06 -4 0
#> 10 -1e+30 0.0000542 25 1.895275e-06 -4 0
#> 11 -1e+30 0.0000339 25 1.895323e-06 -4 0
#> 12 -1e+30 0.0001343 25 1.895109e-06 -4 0
combinef(allf, nodes = nodes, nonnegative = TRUE, keep = "all")
#> Warning in combinef(allf, nodes = nodes, nonnegative = TRUE, keep = "all"):
#> Negative base forecasts are truncated to zero.
#> Time Series:
#> Start = 1
#> End = 12
#> Frequency = 1
#> Total A B AA AB BA BB
#> 1 5.326032 0.3417779 4.984254 0 0.3417779 1.0425411 3.941713
#> 2 5.508210 0.3239697 5.184240 0 0.3239697 1.0319735 4.152267
#> 3 5.690476 0.3063081 5.384168 0 0.3063081 1.0213765 4.362791
#> 4 5.872680 0.2885437 5.584136 0 0.2885437 1.0108002 4.573336
#> 5 6.054928 0.2708514 5.784076 0 0.2708514 1.0002093 4.783867
#> 6 6.237145 0.2531085 5.984036 0 0.2531085 0.9896286 4.994408
#> 7 6.419383 0.2354011 6.183982 0 0.2354011 0.9790409 5.204941
#> 8 6.601607 0.2176689 6.383938 0 0.2176689 0.9684580 5.415480
#> 9 6.783841 0.1999540 6.583887 0 0.1999540 0.9578717 5.626015
#> 10 6.966067 0.1822270 6.783840 0 0.1822270 0.9472879 5.836553
#> 11 7.148299 0.1645085 6.983791 0 0.1645085 0.9367023 6.047088
#> 12 7.330527 0.1467840 7.183743 0 0.1467840 0.9261179 6.257625Created on 2024-05-23 with reprex v2.1.0
Prepare for release:
devtools::build_readme()usethis::use_cran_comments()devtools::check(remote = TRUE, manual = TRUE)devtools::check_win_devel()rhub::check_for_cran()cran-comments.mdSubmit to CRAN:
usethis::use_version('minor')devtools::submit_cran()Wait for CRAN...
usethis::use_github_release()usethis::use_dev_version()
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