# Is it necessary to convert mixed type features to YoY percent change in order to coordinate with target variable

Let's say I'm trying to build a time series model with the following dataset:

import pandas as pd
import numpy as np

np.random.seed(2021)
dates = pd.date_range(start='2020-01-01', end='2021-12-01', freq='MS')
df = pd.DataFrame(np.random.uniform(-1, 1, size=(24, 4)), index=dates, columns=['A_values', 'B_values', 'C_values', 'target'])
df


Out:

            A_values  B_values  C_values    target
2020-01-01  0.211957  0.466739 -0.722106 -0.374654
2020-02-01  0.994487 -0.743675 -0.642014  0.505851
2020-03-01  0.324321  0.568620 -0.806211 -0.882857
......
2021-10-01  0.940398 -0.168986 -0.339022  0.933428
2021-11-01  0.651798 -0.389200  0.646556  0.113821
2021-12-01  0.437002  0.759874 -0.007980  0.024173


Suppose I will use A_values (which is actual values of a variable), B_values (which is month over month percent change of a variable), C_values (which is year over year percent change of a variable) to forecast target column (which is year over year percent change of one variable).

Now my question is should I convert A_values and B_values to year over year percent change to coordinate with the target column's type? If I don't do that, will it lower the prediction accuracy?

In other words, could the machine learning algorithm such as XGBoost capture the relationship between MoM pct change and YoY pct change?

Note: it's easy to convert the actual value to YoY pct change, meanwhile we lose accuracy as converting from MoM to YoY.