The 33 observations are essentially the # of half-days observed per month. Time series analysis does not require that the observations be integers ,just equally spaced . Think of taking the integer data reflecting airline passengers and expressing it in millions. The identified model wouldn't change ,, just the scale of the parameters.
The non-zero data starts at period 4 thus we have an effective # of 30.
Analysis of the data suggests that there are 4 anomalies and a useful model might be with stats here
The Actual,Fit and Forecast graph is here with forecasts shown for the next 12 months . Since anomalies have occurred in the past , they may occur in the future. I used Monte-Carlo bootstrapping to reflect possible future "pulses" rather than to assume symmetric limits based upon an assumption of normally distributed model errors.
All models are wrong .. but this one seems potentially useful.
If one tried to do this separately by employee .... the data might be too sparse i.e. too many 0's to form a useful model.