Refit a TSLM

Applies a fitted TSLM to a new dataset.

# S3 method for TSLM
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)

Arguments

object	The time series model used to produce the forecasts
new_data	A tsibble containing future information used to forecast.
specials	(passed by fabletools::forecast.mdl_df()).
reestimate	If TRUE, the coefficients for the fitted model will be re-estimated to suit the new data.
...	Additional arguments for forecast model methods.

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(TSLM(value ~ trend() + season()))

report(fit)
#> Series: value 
#> Model: TSLM 
#> 
#> Residuals:
#>       Min        1Q    Median        3Q       Max 
#> -422.6976  -58.6048   -0.1143   64.2905  644.0310 
#> 
#> Coefficients:
#>                  Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)     2257.1179    74.8463  30.157  < 2e-16 ***
#> trend()           -4.1060     0.9662  -4.250 7.72e-05 ***
#> season()year2    -44.3940    97.0086  -0.458  0.64890    
#> season()year3   -151.1214    97.0230  -1.558  0.12468    
#> season()year4   -460.1821    97.0471  -4.742 1.38e-05 ***
#> season()year5   -799.2429    97.0807  -8.233 2.21e-11 ***
#> season()year6   -922.4702    97.1240  -9.498 1.71e-13 ***
#> season()year7   -968.5310    97.1768  -9.967 2.91e-14 ***
#> season()year8  -1063.4250    97.2393 -10.936 8.09e-16 ***
#> season()year9  -1075.3190    97.3112 -11.050 5.34e-16 ***
#> season()year10  -851.7131    97.3927  -8.745 3.04e-12 ***
#> season()year11  -711.1071    97.4838  -7.295 8.57e-10 ***
#> season()year12  -288.1679    97.5843  -2.953  0.00451 ** 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 168 on 59 degrees of freedom
#> Multiple R-squared: 0.875,	Adjusted R-squared: 0.8495
#> F-statistic: 34.41 on 12 and 59 DF, p-value: < 2.22e-16

fit %>%
  refit(lung_deaths_female) %>%
  report()
#> Series: value 
#> Model: TSLM 
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -1515.5 -1123.7  -897.9  -748.7  -505.5 
#> 
#> Coefficients:
#>                 Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)     2257.118    475.866   4.743 1.38e-05 ***
#> trend()           -4.106      6.143  -0.668   0.5065    
#> season()year2    -44.394    616.772  -0.072   0.9429    
#> season()year3   -151.121    616.864  -0.245   0.8073    
#> season()year4   -460.182    617.017  -0.746   0.4587    
#> season()year5   -799.243    617.231  -1.295   0.2004    
#> season()year6   -922.470    617.506  -1.494   0.1405    
#> season()year7   -968.531    617.842  -1.568   0.1223    
#> season()year8  -1063.425    618.238  -1.720   0.0907 .  
#> season()year9  -1075.319    618.696  -1.738   0.0874 .  
#> season()year10  -851.713    619.214  -1.375   0.1742    
#> season()year11  -711.107    619.793  -1.147   0.2559    
#> season()year12  -288.168    620.432  -0.464   0.6440    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 1068 on 59 degrees of freedom
#> Multiple R-squared: 0.1476,	Adjusted R-squared: -0.0258
#> F-statistic: 0.8512 on 12 and 59 DF, p-value: 0.59898