Does standardization on data from different categories make them comparable? I've time series for different costs (listed quarterly). These costs are of different types (E.g. employee salaries, outsourcing, travel claims, etc.), and each of them have their own range of values. For example, cost A has range of 300k-400k, while cost B has 1-10k.
I wonder, if I want to compare costs across different categories, does standardizing each cost separately make them comparable? If yes/no, what (additional) procedures I need to make them comparable? On top of that, if I want to build a generic model to either predict future costs/detect anomaly in time series, what additional things I need to pay attention to aside from what I mentioned to make the model feasible?
TIA.

Standardization or Normalization can be used to ensure the data are unitless (not in currency unit). You can select a specific point of time and compare it standardized/normalized unitless cost for employee salaries, outsourcing, travel claims, etc. I may be wrong but I don't think you need standardized values to build a model because that will lead to fitted/estimated standardized dependent variable and converting back to currency unit seems like tedious (read Standardized vs Unstandardized Regression Coefficients). Also, there are more than 2 methods to make the data unitless. If you have outliers or influential observations, you may want to try robust standardization instead of 'standard' standardization.
