Comments (9)
robjhyndman
commented on July 21, 2024
6
This occurs when you have a constant column in your data set. It is impossible to estimate covariances when one of the series is constant. Add some small random noise to the constant column and it should be ok.
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jnuvenus
commented on July 21, 2024
@brunocarlin Hello, I ran into the same problem, have you solved this problem yet? I rounded both the residuals and replaced any 0s with 0.1s or 1s， but still got this error.
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brunocarlin
commented on July 21, 2024
I think our problem was the series full of zeros or some other number, try adding random noise, I will be honest , in my final version I stuck with hts because Mint was too advanced for me
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yasineahmed
commented on July 21, 2024
Hello,
was anyone able to resolve this issue?
thanks.
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yasineahmed
commented on July 21, 2024
Ah, works fine now after adding random noise via rnorm()
Thank you for your assistance.
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dclengacher
commented on July 21, 2024
I added noise to both the forecast and residuals and still get this error.
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medoeje
commented on July 21, 2024
I added noise to both the forecast and residuals and still get this error.
Same here.
When I'm using ets or arima forecasting algorithms it works well (after removing constant columns), but when I use hybridModel I always get the error.
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edoardomichielon
commented on July 21, 2024
Same error here, this time it occurs even if series is not constant, so any of the solution mentioned helps
Thank you for your assistance.
library(dplyr , quietly = TRUE)
library(tsibble, quietly = TRUE)
library(fable , quietly = TRUE)
tsibble::tourism %>%
filter(State == 'ACT') %>%
aggregate_key(State / Region, Trips = sum(Trips)) %>%
model(base = fable::SNAIVE(Trips)) %>%
reconcile(mint_cov = min_trace(base, method = "mint_cov")) %>%
forecast(h = 1)
#> Error in `mutate()`:
#> ! Problem while computing `mint_cov = (function (object, ...) ...`.
#> Caused by error:
#> ! min_trace needs covariance matrix to be positive definite.Created on 2022-05-26 by the reprex package (v2.0.1)
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.3 (2022-03-10)
#> os Ubuntu 20.04.4 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C.UTF-8
#> ctype C.UTF-8
#> tz Etc/UTC
#> date 2022-05-26
#> pandoc 2.11.4 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> anytime 0.3.9 2020-08-27 [2] CRAN (R 4.0.4)
#> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.4)
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#> colorspace 2.0-3 2022-02-21 [2] CRAN (R 4.1.2)
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#> ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.1.1)
#> evaluate 0.15 2022-02-18 [2] CRAN (R 4.1.2)
#> fable * 0.3.1 2021-05-16 [2] CRAN (R 4.1.1)
#> fabletools * 0.3.2.9000 2022-03-16 [2] Github (tidyverts/fabletools@3003027)
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#> progressr 0.10.0 2021-12-19 [2] CRAN (R 4.1.2)
#> purrr 0.3.4 2020-04-17 [2] CRAN (R 4.0.4)
#> R6 2.5.1 2021-08-19 [2] CRAN (R 4.1.1)
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#> vctrs 0.3.8 2021-04-29 [2] CRAN (R 4.1.1)
#> withr 2.5.0 2022-03-03 [2] CRAN (R 4.1.2)
#> xfun 0.30 2022-03-02 [2] CRAN (R 4.1.2)
#> yaml 2.3.5 2022-02-21 [2] CRAN (R 4.1.2)
#>
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#> [2] /usr/local/lib/R/site-library
#> [3] /usr/lib/R/site-library
#> [4] /usr/lib/R/library
#>
#> ──────────────────────────────────────────────────────────────────────────────from hts.
robjhyndman
commented on July 21, 2024
First, this has nothing to do with the hts package. You are using the forecast reconcilation procedures in the fabletools package.
Second, your example consists of a single time series (the ACT), so the reconciliation is degenerate.
Third, SNAIVE is already reconciled since the forecasts are equal to the last year of data, so the reconciliation is degenerate.
That said, we should improve fabletools to prevent the error. @mitchelloharawild
To see it working on a small non-degenerate example, try this:
library(dplyr , quietly = TRUE)
library(tsibble, quietly = TRUE)
library(fable , quietly = TRUE)
tsibble::tourism %>%
filter(State == 'ACT') %>%
aggregate_key(State / Region, Trips = sum(Trips)) %>%
model(base = fable::SNAIVE(Trips)) %>%
reconcile(mint_cov = min_trace(base, method = "mint_cov")) %>%
forecast(h = 1)from hts.
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