Я работал над проектом для университета со следующими данными.
данные
Я выполнил auto.arima в Windows и Ма c и получил другое Результаты. В Ма c я получил и NaN предупреждение. Любая идея, почему это может быть?
install.packages("ggplot2")
install.packages("tseries")
install.packages("forecast")
library(ggplot2)
library(tseries)
library(forecast)
library(readxl)
via_dat <- read_excel("Viajeros.xlsx")
via <- ts(via_dat[,-1], start=c(1999,1), frequency=12)
via_train <- window(via, end=c(2018,12))
fitvia <- auto.arima(log(via_train), seasonal=TRUE)
checkresiduals(fitvia)
print(fitvia)
Вывод в Windows
Series: log(via_train)
ARIMA(4,0,0)(1,1,1)[12] with drift
Coefficients:
ar1 ar2 ar3 ar4 sar1 sma1 drift
0.6280 0.1756 0.0903 -0.0017 0.3322 -0.8509 0.0033
s.e. 0.0675 0.0803 0.0783 0.0676 0.1114 0.0891 0.0005
sigma^2 estimated as 0.001216: log likelihood=441.4
AIC=-866.81 AICc=-866.15 BIC=-839.37
Вывод в Ма c
Series: log(via_train)
ARIMA(4,0,0)(2,1,2)[12] with drift
Coefficients:
Warning message in sqrt(diag(x$var.coef)):
“NaNs produced”
ar1 ar2 ar3 ar4 sar1 sar2 sma1 sma2
0.6157 0.1934 0.0989 -0.0114 -0.3737 0.2908 -0.1539 -0.6143
s.e. NaN NaN NaN 0.0082 NaN NaN 0.0554 0.0649
drift
0.0033
s.e. 0.0005
sigma^2 estimated as 0.001223: log likelihood=441.66
AIC=-863.32 AICc=-862.31 BIC=-829.03
Добавлена информация о системе
Windows:
R version 3.6.2 (2019-12-12)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 16299)
Matrix products: default
locale:
[1] LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252
[3] LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C
[5] LC_TIME=Spanish_Spain.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] readxl_1.3.1 dplyr_0.8.3 zoo_1.8-7
[4] tseries_0.10-47 forecast_8.10 FactoMineR_2.2
[7] corrplot_0.84 cluster_2.1.0 factoextra_1.0.6
[10] ggplot2_3.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 reshape2_1.4.3 purrr_0.3.3
[4] urca_1.3-0 lattice_0.20-38 colorspace_1.4-1
[7] vctrs_0.2.1 utf8_1.1.4 rlang_0.4.2
[10] pillar_1.4.3 glue_1.3.1 withr_2.1.2
[13] TTR_0.23-6 plyr_1.8.5 lifecycle_0.1.0
[16] stringr_1.4.0 quantmod_0.4-15 timeDate_3043.102
[19] munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0
[22] leaps_3.1 labeling_0.3 lmtest_0.9-37
[25] parallel_3.6.2 curl_4.3 fansi_0.4.0
[28] xts_0.12-0 Rcpp_1.0.3 scales_1.1.0
[31] backports_1.1.5 flashClust_1.01-2 scatterplot3d_0.3-41
[34] farver_2.0.1 fracdiff_1.5-1 digest_0.6.23
[37] stringi_1.4.3 ggrepel_0.8.1 grid_3.6.2
[40] quadprog_1.5-8 cli_2.0.0 tools_3.6.2
[43] magrittr_1.5 lazyeval_0.2.2 tibble_2.1.3
[46] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0
[49] MASS_7.3-51.4 assertthat_0.2.1 rstudioapi_0.10
[52] R6_2.4.1 nnet_7.3-12 nlme_3.1-142
[55] compiler_3.6.2
Ма c (после обновления):
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.3.1 dplyr_0.8.3 zoo_1.8-7 tseries_0.10-47
[5] forecast_8.10 FactoMineR_2.1 corrplot_0.84 cluster_2.1.0
[9] factoextra_1.0.6 ggplot2_3.2.1
loaded via a namespace (and not attached):
[1] ggrepel_0.8.1 Rcpp_1.0.2 lubridate_1.7.4
[4] lattice_0.20-38 class_7.3-15 digest_0.6.22
[7] zeallot_0.1.0 utf8_1.1.4 assertthat_0.2.1
[10] glmnet_3.0-2 ipred_0.9-9 lmtest_0.9-37
[13] foreach_1.4.7 cellranger_1.1.0 R6_2.4.0
[16] plyr_1.8.4 backports_1.1.5 stats4_3.6.2
[19] pillar_1.4.2 rlang_0.4.1 curl_4.2
[22] lazyeval_0.2.2 caret_6.0-84 rstudioapi_0.10
[25] data.table_1.12.6 fracdiff_1.5-0 TTR_0.23-6
[28] rpart_4.1-15 Matrix_1.2-18 labeling_0.3
[31] splines_3.6.2 gower_0.2.1 stringr_1.4.0
[34] munsell_0.5.0 compiler_3.6.2 pkgconfig_2.0.3
[37] urca_1.3-0 shape_1.4.4 nnet_7.3-12
[40] flashClust_1.01-2 tidyselect_0.2.5 tibble_2.1.3
[43] prodlim_2019.11.13 quadprog_1.5-8 codetools_0.2-16
[46] fansi_0.4.0 crayon_1.3.4 withr_2.1.2
[49] MASS_7.3-51.4 leaps_3.0 recipes_0.1.8
[52] ModelMetrics_1.2.2 grid_3.6.2 nlme_3.1-142
[55] gtable_0.3.0 magrittr_1.5 pROC_1.15.3
[58] scales_1.0.0 quantmod_0.4-15 cli_1.1.0
[61] stringi_1.4.3 reshape2_1.4.3 scatterplot3d_0.3-41
[64] timeDate_3043.102 vctrs_0.2.0 xts_0.12-0
[67] generics_0.0.2 lava_1.6.6 iterators_1.0.12
[70] tools_3.6.2 glue_1.3.1 purrr_0.3.3
[73] parallel_3.6.2 survival_3.1-8 colorspace_1.4-1