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I am trying to understand what is the best possible way to execute a time series forecast. I am trying to forecast the number of employees that are going to call in sick on a given day. My data has the current features.

  • Employee hometown
  • Job title of employee
  • Seniority of employee
  • Date
  • Day of week
  • Week of Year
  • Sick count

The Employee hometown, job title, and seniority columns are important because sick numbers historically are heavily dependent on these attributes. Also, I would like to execute an algorithm that also puts a heavier weight on 2021 and 2022 sick counts.

Any ideas would be greatly appreciated!

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  • $\begingroup$ can you add an example of your dataset? It seems your features defines an individual employee, but your target informs about a number of sick calls instead of a binary yes/no call? To confirm the way you aggregate your data $\endgroup$ Commented Aug 24, 2022 at 16:39

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I assume that sick count is a numerical variable. If so, you could pre-process your data in a way to give a higher value to sick counts for the years $2021$ and $2022$. To make an example, if your sick count for the year $2021$ in the original data is $x = 10$ you could do $5 \cdot x = 50$ to give a higher value on such counts. It is not guaranteed that such a trick will work, but at least you could give it a try as it depends on the forecasting algorithm.

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