Model creating 1 model for each row

Question

I have a timeseries and i want to create a regression model with it, the time series looks as follows :

  Date       Value PREDICTOR1 PREDICTOR2 PREDICTOR3 PREDICTOR4 PREDICTOR5 PREDICTOR6 PREDICTOR7 PREDICTOR8 PREDICTOR9 PREDICTOR10 PREDICTOR11 PREDICTOR12
  <date>     <dbl>         <dbl>              <dbl>      <dbl>             <dbl>             <dbl>     <dbl>        <dbl>                  <dbl>               <dbl>
1 2021-09-02  74             0.1                3.7        3.8               0.6               1.5      63.2          2.6                 -51900                 1.6
2 2021-09-03  74.4           0.1                3.7        3.8               0.6               1.5      63.2          2.6                 -51900                 1.6
3 2021-09-07  73.9           0.1                3.7        3.8               0.6               1.5      63.2          2.6                 -51900                 1.6
4 2021-09-08  73.7           0.1                3.7        3.8               0.6               1.5      63.2          2.6                 -51900                 1.6
5 2021-09-09  73.8           0.1                3.7        3.8               0.6               1.5      63.2          2.6                 -51900                 1.6
6 2021-09-10  73.7           0.1                3.7        3.8               0.6               1.5      63.2          2.6                 -51900                 1.6

From it I trained a model :

fit <-    df %>%
      model(
        tslm = TSLM(Value ~ PREDICTOR1+ PREDICTOR2+ PREDICTOR3+ PREDICTOR4+ PREDICTOR5 +PREDICTOR6+ PREDICTOR7+ PREDICTOR8 +PREDICTOR9 +PREDICTOR10 +PREDICTOR11 +PREDICTOR12)
    )

But I receive as result for report :

> report(fit)
# A tibble: 3,409 x 16
      id .model r_squared adj_r_squared sigma2 statistic p_value    df log_lik   AIC  AICc   BIC    CV deviance df.residual  rank
   <int> <chr>      <dbl>         <dbl>  <dbl>     <dbl>   <dbl> <int>   <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>       <int> <int>
 1     1 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 2     2 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 3     3 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 4     4 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 5     5 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 6     6 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 7     7 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 8     8 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
 9     9 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1
10    10 tslm         NaN           NaN    NaN       NaN     NaN     1     Inf  -Inf  -Inf  -Inf   NaN        0           0     1

So it created a model for each row of data in df (>3000), all of them non-usable.

Does somebody has a hint ?

P.S. Is my first time

Answer 1

I don't know why this is not working. It must be something in your data or R setup.
Here is an example of how this should work:

library(fable)

aq <- cbind(Date = as.Date(paste('2021', airquality$Month, airquality$Day, sep = '-')), airquality) |> 
  as_tsibble()
fit <- aq |> model(tslm = TSLM(Ozone ~ Solar.R + Wind + Temp))
report(fit)
Series: Ozone 
Model: TSLM 

Residuals:
    Min      1Q  Median      3Q     Max 
-40.485 -14.219  -3.551  10.097  95.619 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -64.34208   23.05472  -2.791  0.00623 ** 
Solar.R       0.05982    0.02319   2.580  0.01124 *  
Wind         -3.33359    0.65441  -5.094 1.52e-06 ***
Temp          1.65209    0.25353   6.516 2.42e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21.18 on 107 degrees of freedom
Multiple R-squared: 0.6059, Adjusted R-squared: 0.5948
F-statistic: 54.83 on 3 and 107 DF, p-value: < 2.22e-16