R：通过寓言，斜纹和地图预测多个时间序列

Question

I am trying to fit some time series using the R packages tsibble and fable , the still-under-construction replacement for the redoubtable Rob Hyndman's forecast package. The series are all combined into one tsibble, which I then fit with ARIMA, a function which replaces, among other things, forecast::auto.arima .

I use map_at , first to iterate over all the elements except the Date , and then again to extract the model information from the models that have been fit to each series using fablelite::components . (A lot of the fable functions are really in fablelite ).

This fails, apparently because components expects an object of class mdl_df and my model objects have class mdl_defn

Here is a toy example that (almost) reproduces the error:

library(tidyverse)
library(tsibble)
library(fable)
set.seed(1)
ar1  <-  arima.sim(model=list(ar=.6), n=10)
ma1 <- arima.sim(model=list(ma=0.4), n=10)
Date  <- c(ymd("2019-01-01"):ymd("2019-01-10"),  ymd("2019-01-01"):ymd("2019-01-10"))
tb <- tibble(Date, ar1, ma1)

# Fit the whole series
tb_all <- tb   %>% 
map_at(.at =  c("ar1", "ma1"), .f = ARIMA)
names(arima_all[2:3])<- c("ar1", "ma1")

# Extract model components
tb_components <- tb %>%  
  map_at(.at = c("ar1", "ma1"), 
         .f = fablelite::components)

Note that in this toy, like my real data, time is in 5-day weeks with missing weekends

In this toy example, the the error message says the components function rejects list elements on grounds there is no method for class ts . In my real case, which uses longer series and more of them, but is to my eye otherwise identical, elements are rejected because they are of class mdl_defn . Note that if I examine the 2nd and third elements of tb_all with str( ) , they also display as of Classes 'mdl_defn' , 'R6' Not sure where the ts in the error message comes from.

Answer 1

Here is an example that hopefully does something like what you want.

First, you need to create a tsibble:

library(tidyverse)
library(tsibble)
library(fable)
library(lubridate)
set.seed(1)
ar1  <-  arima.sim(model=list(ar=.6), n=30)
ma1 <- arima.sim(model=list(ma=0.4), n=30)
Date  <- ymd(paste0("2019-01-",1:30))
tb <- bind_cols(Date=Date, ar1=ar1, ma1=ma1) %>%
  gather("Series", "value", -Date) %>%
  as_tsibble(index=Date, key=Series)
tb
#> # A tsibble: 60 x 3 [1D]
#> # Key:       Series [2]
#>    Date       Series   value
#>    <date>     <chr>    <dbl>
#>  1 2019-01-01 ar1    -2.07  
#>  2 2019-01-02 ar1    -0.118 
#>  3 2019-01-03 ar1    -0.116 
#>  4 2019-01-04 ar1    -0.0856
#>  5 2019-01-05 ar1     0.892 
#>  6 2019-01-06 ar1     1.36  
#>  7 2019-01-07 ar1     1.41  
#>  8 2019-01-08 ar1     1.76  
#>  9 2019-01-09 ar1     1.84  
#> 10 2019-01-10 ar1     1.18  
#> # … with 50 more rows

This contains two series: ar1 and ma1 over the same 30 days.

Next you can fit ARIMA models to both series in one simple function.

tb_all <- tb %>% model(arima = ARIMA(value))
tb_all
#> # A mable: 2 x 2
#> # Key:     Series [2]
#>   Series arima                 
#>   <chr>  <model>               
#> 1 ar1    <ARIMA(0,0,2)>        
#> 2 ma1    <ARIMA(0,0,0) w/ mean>

Finally, it is not clear what you are trying to extract using components() , but perhaps one of the following does what you want:

tidy(tb_all)
#> # A tibble: 3 x 7
#>   Series .model term     estimate std.error statistic  p.value
#>   <chr>  <chr>  <chr>       <dbl>     <dbl>     <dbl>    <dbl>
#> 1 ar1    arima  ma1         0.810     0.198      4.09 0.000332
#> 2 ar1    arima  ma2         0.340     0.181      1.88 0.0705  
#> 3 ma1    arima  constant    0.295     0.183      1.61 0.118
glance(tb_all)
#> # A tibble: 2 x 9
#>   Series .model sigma2 log_lik   AIC  AICc   BIC ar_roots  ma_roots 
#>   <chr>  <chr>   <dbl>   <dbl> <dbl> <dbl> <dbl> <list>    <list>   
#> 1 ar1    arima   0.695   -36.4  78.9  79.8  83.1 <cpl [0]> <cpl [2]>
#> 2 ma1    arima   1.04    -42.7  89.4  89.8  92.2 <cpl [0]> <cpl [0]>
augment(tb_all)
#> # A tsibble: 60 x 6 [1D]
#> # Key:       Series, .model [2]
#>    Series .model Date         value .fitted  .resid
#>    <chr>  <chr>  <date>       <dbl>   <dbl>   <dbl>
#>  1 ar1    arima  2019-01-01 -2.07    -0.515 -1.56  
#>  2 ar1    arima  2019-01-02 -0.118   -1.21   1.09  
#>  3 ar1    arima  2019-01-03 -0.116    0.511 -0.627 
#>  4 ar1    arima  2019-01-04 -0.0856  -0.155  0.0690
#>  5 ar1    arima  2019-01-05  0.892   -0.154  1.05  
#>  6 ar1    arima  2019-01-06  1.36     0.871  0.486 
#>  7 ar1    arima  2019-01-07  1.41     0.749  0.659 
#>  8 ar1    arima  2019-01-08  1.76     0.699  1.06  
#>  9 ar1    arima  2019-01-09  1.84     1.09   0.754 
#> 10 ar1    arima  2019-01-10  1.18     0.973  0.206 
#> # … with 50 more rows

To see model outputs in the traditional way, use report() :

tb_all %>% filter(Series=='ar1') %>% report()
#> Series: value 
#> Model: ARIMA(0,0,2) 
#> 
#> Coefficients:
#>          ma1     ma2
#>       0.8102  0.3402
#> s.e.  0.1982  0.1809
#> 
#> sigma^2 estimated as 0.6952:  log likelihood=-36.43
#> AIC=78.86   AICc=79.78   BIC=83.06
tb_all %>% filter(Series=='ma1') %>% report()
#> Series: value 
#> Model: ARIMA(0,0,0) w/ mean 
#> 
#> Coefficients:
#>       constant
#>         0.2950
#> s.e.    0.1833
#> 
#> sigma^2 estimated as 1.042:  log likelihood=-42.68
#> AIC=89.36   AICc=89.81   BIC=92.17