Closed robjhyndman closed 5 years ago
Also, the first model above is incorrect. It should be an ARIMA(1,1,1).
forecast::auto.arima(WWWusage)
#> Series: WWWusage
#> ARIMA(1,1,1)
#>
#> Coefficients:
#> ar1 ma1
#> 0.6504 0.5256
#> s.e. 0.0842 0.0896
#>
#> sigma^2 estimated as 9.995: log likelihood=-254.15
#> AIC=514.3 AICc=514.55 BIC=522.08
Created on 2019-04-29 by the reprex package (v0.2.1)
Incorrect model selection is an issue with feasts: Moved to https://github.com/tidyverts/feasts/issues/40
How exactly are the models named inconsistently? The first model includes a constant, which is currently being treated/displayed as a LM term (is this not how we include constants in the presence of differences?).
Is the inclusion of the constant substantially different (in implementation, not resulting forecasts) for choices of d
and D
? That is to say, is including a xreg
intercept column different from calling arima(..., include.mean = TRUE)
.
The model summary (LM w/ ARIMA(0,0,0) errors
) can/should be improved... Thoughts on this?
Your bulleted scheme looks ok to me. mod1
is a constant with d+D==0, so according to this scheme it should be "ARIMA(p,d,q)(P,D,Q)[m] w/ mean".
library(tidyverse)
library(tsibble)
#>
#> Attaching package: 'tsibble'
#> The following object is masked from 'package:dplyr':
#>
#> id
library(fable)
#> Loading required package: fablelite
fit <- as_tsibble(WWWusage) %>%
model(
mod1 = ARIMA(value),
mod2 = ARIMA(value ~ pdq(d=1))
)
fit %>% select(mod1) %>% report()
#> Series: value
#> Model: ARIMA(0,0,0) w/ mean
#>
#> Coefficients:
#> constant
#> 137.0800
#> s.e. 3.9799
#>
#> sigma^2 estimated as 1600: log likelihood=-510.28
#> AIC=1024.56 AICc=1024.81 BIC=1032.37
fit %>% select(mod2) %>% report()
#> Series: value
#> Model: ARIMA(1,1,1)
#>
#> Coefficients:
#> ar1 ma1
#> 0.6504 0.5256
#> s.e. 0.0842 0.0896
#>
#> sigma^2 estimated as 9.995: log likelihood=-254.15
#> AIC=514.3 AICc=514.94 BIC=527.28
Created on 2019-05-01 by the reprex package (v0.2.1)
Created on 2019-04-29 by the reprex package (v0.2.1)