Trouble using forecast::auto.arima() with fabletools::model() - "no ARIMA models to choose from" #392

kenahoo · 2024-01-29T18:28:07Z

Hi,

I'm having trouble using forecast::auto.arima() to determine parameters, and then using fabletools::model() to train based on those parameters. Certain data sets seem to come up with parameters that model() doesn't like. For these data sets, it dies with the error There are no ARIMA models to choose from after imposing the `order_constraint`, please consider allowing more models.

I've attached a working example and a non-working example.

training-noerror.rds.zip
training-error.rds.zip

(I had to convert the .rds files to .zip or else it seems that GitHub doesn't let them be uploaded - each zip file just has the single .rds file indicated.)

Here's my test code as a reprex:

suppressPackageStartupMessages({
  library(dplyr)
  library(tsibble)
  library(fable)
})
# We also use 'forecast' and 'fabletools' below

train_model <- function(X, y) {
  #use auto.arima to get model specification rather than using fable. auto.arima is generally much faster
  arima_fit <- forecast::auto.arima(
    y=ts(y %>% as.ts(), frequency = 365.25),
    xreg=X %>% as.data.frame() %>% select(-date) %>% as.matrix()
  )
  cat("##### Auto ARIMA fit:", "\n")
  print(arima_fit)

  pdq_form <- sprintf('pdq(%s,%s,%s)', arima_fit$arma[1], arima_fit$arma[6], arima_fit$arma[2])
  arima_form <- formula(paste('target ~ x1 + ', pdq_form))

  cat("\narima_formula: ", format(arima_form), "\n")

  training <- inner_join(X, y, by="date") %>%
    as_tsibble(index=date)

  fabletools::model(training, ARIMA(arima_form))
}


# Works:
training <- readRDS('~/Downloads/training-noerror.rds')
fit <- train_model(X = training %>% select(x1), y = training %>% select(target))
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo
#> ##### Auto ARIMA fit: 
#> Series: ts(y %>% as.ts(), frequency = 365.25) 
#> Regression with ARIMA(2,1,2) errors 
#> 
#> Coefficients:
#>          ar1      ar2      ma1     ma2      x1
#>       0.7686  -0.1705  -1.2373  0.3404  0.0031
#> s.e.  0.6741   0.2510   0.6745  0.5690  0.0004
#> 
#> sigma^2 = 0.2235:  log likelihood = -488.13
#> AIC=988.26   AICc=988.37   BIC=1015.83
#> 
#> arima_formula:  target ~ x1 + pdq(2, 1, 2)

# Fails:
training2 <- readRDS('~/Downloads/training-error.rds')
fit <- train_model(X = training2 %>% select(x1), y = training2 %>% select(target))
#> ##### Auto ARIMA fit: 
#> Series: ts(y %>% as.ts(), frequency = 365.25) 
#> Regression with ARIMA(5,1,3) errors 
#> 
#> Coefficients:
#>           ar1      ar2     ar3     ar4     ar5      ma1     ma2      ma3     x1
#>       -0.3714  -0.4704  0.3603  0.0888  0.0572  -0.0890  0.0609  -0.7744  2e-03
#> s.e.   0.0913   0.0891  0.0676  0.0487  0.0464   0.0835  0.0906   0.0733  5e-04
#> 
#> sigma^2 = 0.3704:  log likelihood = -670.28
#> AIC=1360.55   AICc=1360.86   BIC=1406.49
#> 
#> arima_formula:  target ~ x1 + pdq(5, 1, 3)
#> Warning: 1 error encountered for ARIMA(arima_form)
#> [1] There are no ARIMA models to choose from after imposing the `order_constraint`, please consider allowing more models.

^{Created on 2024-01-29 with reprex v2.1.0}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.2 (2023-10-31)
#>  os       macOS Sonoma 14.3
#>  system   aarch64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       UTC
#>  date     2024-01-29
#>  pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package        * version  date (UTC) lib source
#>  anytime          0.3.9    2020-08-27 [1] CRAN (R 4.3.0)
#>  cli              3.6.1    2023-03-23 [1] CRAN (R 4.3.0)
#>  colorspace       2.1-0    2023-01-23 [1] CRAN (R 4.3.0)
#>  curl             5.1.0    2023-10-02 [1] CRAN (R 4.3.1)
#>  digest           0.6.33   2023-07-07 [1] CRAN (R 4.3.0)
#>  distributional   0.3.2    2023-03-22 [1] CRAN (R 4.3.0)
#>  dplyr          * 1.1.3    2023-09-03 [1] CRAN (R 4.3.0)
#>  ellipsis         0.3.2    2021-04-29 [1] CRAN (R 4.3.0)
#>  evaluate         0.22     2023-09-29 [1] CRAN (R 4.3.1)
#>  fable          * 0.3.2    2022-09-01 [1] CRAN (R 4.3.2)
#>  fabletools     * 0.3.3    2023-04-04 [1] CRAN (R 4.3.0)
#>  fansi            1.0.4    2023-01-22 [1] CRAN (R 4.3.0)
#>  farver           2.1.1    2022-07-06 [1] CRAN (R 4.3.0)
#>  fastmap          1.1.1    2023-02-24 [1] CRAN (R 4.3.0)
#>  feasts           0.3.1    2023-03-22 [1] CRAN (R 4.3.0)
#>  forecast         8.21.1   2023-08-31 [1] CRAN (R 4.3.0)
#>  fracdiff         1.5-2    2022-10-31 [1] CRAN (R 4.3.0)
#>  fs               1.6.3    2023-07-20 [1] CRAN (R 4.3.0)
#>  generics         0.1.3    2022-07-05 [1] CRAN (R 4.3.0)
#>  ggplot2          3.4.3    2023-08-14 [1] CRAN (R 4.3.0)
#>  glue             1.6.2    2022-02-24 [1] CRAN (R 4.3.0)
#>  gtable           0.3.4    2023-08-21 [1] CRAN (R 4.3.0)
#>  htmltools        0.5.6    2023-08-10 [1] CRAN (R 4.3.0)
#>  knitr            1.44     2023-09-11 [1] CRAN (R 4.3.0)
#>  lattice          0.21-9   2023-10-01 [2] CRAN (R 4.3.2)
#>  lifecycle        1.0.3    2022-10-07 [1] CRAN (R 4.3.0)
#>  lmtest           0.9-40   2022-03-21 [1] CRAN (R 4.3.0)
#>  lubridate        1.9.3    2023-09-27 [1] CRAN (R 4.3.1)
#>  magrittr         2.0.3    2022-03-30 [1] CRAN (R 4.3.0)
#>  munsell          0.5.0    2018-06-12 [1] CRAN (R 4.3.0)
#>  nlme             3.1-163  2023-08-09 [2] CRAN (R 4.3.2)
#>  nnet             7.3-19   2023-05-03 [2] CRAN (R 4.3.2)
#>  pillar           1.9.0    2023-03-22 [1] CRAN (R 4.3.0)
#>  pkgconfig        2.0.3    2019-09-22 [1] CRAN (R 4.3.0)
#>  progressr        0.14.0   2023-08-10 [1] CRAN (R 4.3.0)
#>  purrr            1.0.2    2023-08-10 [1] CRAN (R 4.3.0)
#>  quadprog         1.5-8    2019-11-20 [1] CRAN (R 4.3.0)
#>  quantmod         0.4.25   2023-08-22 [1] CRAN (R 4.3.0)
#>  R6               2.5.1    2021-08-19 [1] CRAN (R 4.3.0)
#>  Rcpp             1.0.11   2023-07-06 [1] CRAN (R 4.3.0)
#>  reprex           2.1.0    2024-01-11 [1] CRAN (R 4.3.1)
#>  rlang            1.1.1    2023-04-28 [1] CRAN (R 4.3.0)
#>  rmarkdown        2.25     2023-09-18 [1] CRAN (R 4.3.1)
#>  rstudioapi       0.15.0   2023-07-07 [1] CRAN (R 4.3.0)
#>  scales           1.2.1    2022-08-20 [1] CRAN (R 4.3.0)
#>  sessioninfo      1.2.2    2021-12-06 [1] CRAN (R 4.3.0)
#>  tibble           3.2.1    2023-03-20 [1] CRAN (R 4.3.0)
#>  tidyr            1.3.0    2023-01-24 [1] CRAN (R 4.3.0)
#>  tidyselect       1.2.0    2022-10-10 [1] CRAN (R 4.3.0)
#>  timechange       0.2.0    2023-01-11 [1] CRAN (R 4.3.0)
#>  timeDate         4022.108 2023-01-07 [1] CRAN (R 4.3.0)
#>  tseries          0.10-54  2023-05-02 [1] CRAN (R 4.3.0)
#>  tsibble        * 1.1.3    2022-10-09 [1] CRAN (R 4.3.0)
#>  TTR              0.24.3   2021-12-12 [1] CRAN (R 4.3.0)
#>  urca             1.3-3    2022-08-29 [1] CRAN (R 4.3.0)
#>  utf8             1.2.3    2023-01-31 [1] CRAN (R 4.3.0)
#>  vctrs            0.6.3    2023-06-14 [1] CRAN (R 4.3.0)
#>  withr            2.5.1    2023-09-26 [1] CRAN (R 4.3.1)
#>  xfun             0.40     2023-08-09 [1] CRAN (R 4.3.0)
#>  xts              0.13.1   2023-04-16 [1] CRAN (R 4.3.0)
#>  yaml             2.3.7    2023-01-23 [1] CRAN (R 4.3.0)
#>  zoo              1.8-12   2023-04-13 [1] CRAN (R 4.3.0)
#> 
#>  [1] /Users/kwilliams/R/library/4.3
#>  [2] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

Any help or insight would be greatly appreciated!

The text was updated successfully, but these errors were encountered:

mitchelloharawild · 2024-01-29T22:02:53Z

The error is occurring due to the unspecified intercept triggering model selection (and hence the order_constraint). When the model isn't fully specified (including intercepts) ARIMA will attempt to automatically select the best model subject to the constraints (selected pdq, and other options like order_constraint).

To fully match the model from forecast::auto.arima() you should specify the presence of the intercept/constant with y ~ 1 or remove the intercept/constant with y ~ 0.

kenahoo · 2024-01-30T02:10:52Z

Thanks for your quick reply, Mitchell - I'll give this a shot tomorrow!

kenahoo · 2024-01-30T17:17:32Z

I got a chance to try it out - looks like I also needed to fill in the PDQ parameters, so my final code looks like the following. Thanks for pointing me in the right direction!

suppressPackageStartupMessages({
  library(dplyr)
  library(tsibble)
  library(fable)
})
# We also use 'forecast' and 'fabletools' below

train_model <- function(X, y) {
  #use auto.arima to get model specification rather than using fable. auto.arima is generally much faster
  arima_fit <- forecast::auto.arima(
    y=ts(y %>% as.ts(), frequency = 365.25),
    xreg=X %>% as.data.frame() %>% select(-date) %>% as.matrix()
  )
  cat("##### Auto ARIMA fit:", "\n")
  print(arima_fit)

  arima_order <- arima_fit$arma[c(1, 6, 2, 3, 7, 4, 5)]

  pdq_form <- sprintf('pdq(%s,%s,%s)', arima_order[1], arima_order[2], arima_order[3])
  PDQ_form <- sprintf('PDQ(%s,%s,%s,period=%s)', arima_order[4], arima_order[5], arima_order[6], period=arima_order[7])

  arima_form <- formula(paste('target ~ x1 +', pdq_form, '+', PDQ_form, '+ 1'))

  cat("\narima_formula: ", format(arima_form), "\n")

  training <- inner_join(X, y, by="date") %>%
    as_tsibble(index=date)

  fabletools::model(training, ARIMA(arima_form))
}


# Works:
training <- readRDS('~/Downloads/training-noerror.rds')
fit <- train_model(X = training %>% select(x1), y = training %>% select(target))
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo
#> ##### Auto ARIMA fit: 
#> Series: ts(y %>% as.ts(), frequency = 365.25) 
#> Regression with ARIMA(2,1,2) errors 
#> 
#> Coefficients:
#>          ar1      ar2      ma1     ma2      x1
#>       0.7686  -0.1705  -1.2373  0.3404  0.0031
#> s.e.  0.6741   0.2510   0.6745  0.5690  0.0004
#> 
#> sigma^2 = 0.2235:  log likelihood = -488.13
#> AIC=988.26   AICc=988.37   BIC=1015.83
#> 
#> arima_formula:  target ~ x1 + pdq(2, 1, 2) + PDQ(0, 0, 0, period = 365) + 1

# Now works too, with PDQ parameters included:
training2 <- readRDS('~/Downloads/training-error.rds')
fit <- train_model(X = training2 %>% select(x1), y = training2 %>% select(target))
#> ##### Auto ARIMA fit: 
#> Series: ts(y %>% as.ts(), frequency = 365.25) 
#> Regression with ARIMA(5,1,3) errors 
#> 
#> Coefficients:
#>           ar1      ar2     ar3     ar4     ar5      ma1     ma2      ma3     x1
#>       -0.3714  -0.4704  0.3603  0.0888  0.0572  -0.0890  0.0609  -0.7744  2e-03
#> s.e.   0.0913   0.0891  0.0676  0.0487  0.0464   0.0835  0.0906   0.0733  5e-04
#> 
#> sigma^2 = 0.3704:  log likelihood = -670.28
#> AIC=1360.55   AICc=1360.86   BIC=1406.49
#> 
#> arima_formula:  target ~ x1 + pdq(5, 1, 3) + PDQ(0, 0, 0, period = 365) + 1

^{Created on 2024-01-30 with reprex v2.1.0}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.2 (2023-10-31)
#>  os       macOS Sonoma 14.3
#>  system   aarch64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       UTC
#>  date     2024-01-30
#>  pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package        * version  date (UTC) lib source
#>  anytime          0.3.9    2020-08-27 [1] CRAN (R 4.3.0)
#>  cli              3.6.1    2023-03-23 [1] CRAN (R 4.3.0)
#>  colorspace       2.1-0    2023-01-23 [1] CRAN (R 4.3.0)
#>  curl             5.1.0    2023-10-02 [1] CRAN (R 4.3.1)
#>  digest           0.6.33   2023-07-07 [1] CRAN (R 4.3.0)
#>  distributional   0.3.2    2023-03-22 [1] CRAN (R 4.3.0)
#>  dplyr          * 1.1.3    2023-09-03 [1] CRAN (R 4.3.0)
#>  ellipsis         0.3.2    2021-04-29 [1] CRAN (R 4.3.0)
#>  evaluate         0.22     2023-09-29 [1] CRAN (R 4.3.1)
#>  fable          * 0.3.2    2022-09-01 [1] CRAN (R 4.3.2)
#>  fabletools     * 0.3.3    2023-04-04 [1] CRAN (R 4.3.0)
#>  fansi            1.0.4    2023-01-22 [1] CRAN (R 4.3.0)
#>  farver           2.1.1    2022-07-06 [1] CRAN (R 4.3.0)
#>  fastmap          1.1.1    2023-02-24 [1] CRAN (R 4.3.0)
#>  forecast         8.21.1   2023-08-31 [1] CRAN (R 4.3.0)
#>  fracdiff         1.5-2    2022-10-31 [1] CRAN (R 4.3.0)
#>  fs               1.6.3    2023-07-20 [1] CRAN (R 4.3.0)
#>  generics         0.1.3    2022-07-05 [1] CRAN (R 4.3.0)
#>  ggplot2          3.4.3    2023-08-14 [1] CRAN (R 4.3.0)
#>  glue             1.6.2    2022-02-24 [1] CRAN (R 4.3.0)
#>  gtable           0.3.4    2023-08-21 [1] CRAN (R 4.3.0)
#>  htmltools        0.5.6    2023-08-10 [1] CRAN (R 4.3.0)
#>  knitr            1.44     2023-09-11 [1] CRAN (R 4.3.0)
#>  lattice          0.21-9   2023-10-01 [2] CRAN (R 4.3.2)
#>  lifecycle        1.0.3    2022-10-07 [1] CRAN (R 4.3.0)
#>  lmtest           0.9-40   2022-03-21 [1] CRAN (R 4.3.0)
#>  lubridate        1.9.3    2023-09-27 [1] CRAN (R 4.3.1)
#>  magrittr         2.0.3    2022-03-30 [1] CRAN (R 4.3.0)
#>  munsell          0.5.0    2018-06-12 [1] CRAN (R 4.3.0)
#>  nlme             3.1-163  2023-08-09 [2] CRAN (R 4.3.2)
#>  nnet             7.3-19   2023-05-03 [2] CRAN (R 4.3.2)
#>  pillar           1.9.0    2023-03-22 [1] CRAN (R 4.3.0)
#>  pkgconfig        2.0.3    2019-09-22 [1] CRAN (R 4.3.0)
#>  progressr        0.14.0   2023-08-10 [1] CRAN (R 4.3.0)
#>  purrr            1.0.2    2023-08-10 [1] CRAN (R 4.3.0)
#>  quadprog         1.5-8    2019-11-20 [1] CRAN (R 4.3.0)
#>  quantmod         0.4.25   2023-08-22 [1] CRAN (R 4.3.0)
#>  R6               2.5.1    2021-08-19 [1] CRAN (R 4.3.0)
#>  Rcpp             1.0.11   2023-07-06 [1] CRAN (R 4.3.0)
#>  reprex           2.1.0    2024-01-11 [1] CRAN (R 4.3.1)
#>  rlang            1.1.1    2023-04-28 [1] CRAN (R 4.3.0)
#>  rmarkdown        2.25     2023-09-18 [1] CRAN (R 4.3.1)
#>  rstudioapi       0.15.0   2023-07-07 [1] CRAN (R 4.3.0)
#>  scales           1.2.1    2022-08-20 [1] CRAN (R 4.3.0)
#>  sessioninfo      1.2.2    2021-12-06 [1] CRAN (R 4.3.0)
#>  tibble           3.2.1    2023-03-20 [1] CRAN (R 4.3.0)
#>  tidyr            1.3.0    2023-01-24 [1] CRAN (R 4.3.0)
#>  tidyselect       1.2.0    2022-10-10 [1] CRAN (R 4.3.0)
#>  timechange       0.2.0    2023-01-11 [1] CRAN (R 4.3.0)
#>  timeDate         4022.108 2023-01-07 [1] CRAN (R 4.3.0)
#>  tseries          0.10-54  2023-05-02 [1] CRAN (R 4.3.0)
#>  tsibble        * 1.1.3    2022-10-09 [1] CRAN (R 4.3.0)
#>  TTR              0.24.3   2021-12-12 [1] CRAN (R 4.3.0)
#>  urca             1.3-3    2022-08-29 [1] CRAN (R 4.3.0)
#>  utf8             1.2.3    2023-01-31 [1] CRAN (R 4.3.0)
#>  vctrs            0.6.3    2023-06-14 [1] CRAN (R 4.3.0)
#>  withr            2.5.1    2023-09-26 [1] CRAN (R 4.3.1)
#>  xfun             0.40     2023-08-09 [1] CRAN (R 4.3.0)
#>  xts              0.13.1   2023-04-16 [1] CRAN (R 4.3.0)
#>  yaml             2.3.7    2023-01-23 [1] CRAN (R 4.3.0)
#>  zoo              1.8-12   2023-04-13 [1] CRAN (R 4.3.0)
#> 
#>  [1] /Users/kwilliams/R/library/4.3
#>  [2] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

mitchelloharawild · 2024-01-31T05:21:41Z

Great, glad it worked out for you!

mitchelloharawild closed this as completed Jan 31, 2024

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Trouble using forecast::auto.arima() with fabletools::model() - "no ARIMA models to choose from" #392

Trouble using forecast::auto.arima() with fabletools::model() - "no ARIMA models to choose from" #392

kenahoo commented Jan 29, 2024 •

edited

Loading

mitchelloharawild commented Jan 29, 2024

kenahoo commented Jan 30, 2024

kenahoo commented Jan 30, 2024

mitchelloharawild commented Jan 31, 2024

Trouble using forecast::auto.arima() with fabletools::model() - "no ARIMA models to choose from" #392

Trouble using forecast::auto.arima() with fabletools::model() - "no ARIMA models to choose from" #392

Comments

kenahoo commented Jan 29, 2024 • edited Loading

mitchelloharawild commented Jan 29, 2024

kenahoo commented Jan 30, 2024

kenahoo commented Jan 30, 2024

mitchelloharawild commented Jan 31, 2024

kenahoo commented Jan 29, 2024 •

edited

Loading