I’ve a Employee Absenteeism data which has 21 variables and 740 rows. The objective of the study is: How much losses every month can we project in 2011 if same trend of absenteeism continues?

I have grouped the data based on month to get the total absent time for that month. The below is the data frame.

```
Category x
1 1 177.6050
2 2 276.1621
3 3 458.7209
4 4 238.6340
5 5 266.1499
6 6 243.5155
7 7 376.9841
8 8 250.3904
9 9 182.4135
10 10 293.2177
11 11 267.1706
12 12 193.9427
```

When I’m fitting this data on time series linear model I get this:

data = ts(aggre.absent.hours.months$x, frequency = 12, start = 2010)

fit = tslm(data ~ trend + season)

summary(fit)

tslm(formula = data ~ trend + season)

Residuals:

ALL 12 residuals are 0: no residual degrees of freedom!

```
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 176.120 NA NA NA
trend 1.485 NA NA NA
season2 97.072 NA NA NA
season3 278.145 NA NA NA
season4 56.573 NA NA NA
season5 82.604 NA NA NA
season6 58.484 NA NA NA
season7 190.468 NA NA NA
season8 62.389 NA NA NA
season9 -7.073 NA NA NA
season10 102.245 NA NA NA
season11 74.713 NA NA NA
season12 NA NA NA NA
```

I know the reason why linear regression gives NA as we don’t have enough samples(rows) as compared to the predicted variable.

Question is how can we forecast the monthly absent time when we have only one year data with trend? Am I missing something here.

Data Set.zip (20.4 KB)

Attached original data set.

Your help will me much appreciated! Thanks in advance!