Coefficients of additional regressors and contributions to the result

Good day!

I have built a model with Profet and the prediction results are very good. But in my model there are 4 additional regressors (for simplicity - A, B, C, D) and I need to get their coefficients and how much the forecast result depends on each of them (contributions of the regressors). I am using regressor_coefficients () function and I get:

regressor | regressor_mode | center | coef_lower | coef | coef_upper
A | additive | 1581.334616 | 0.000153 | 0.000153 | 0.000153
B | additive | 655.913061 | -0.003166 | -0.003166 | -0.003166
C | additive | 0.000000 | 0.000000 | 0.000000 | 0.000000
D | additive | 0.000000 | 0.000000 | 0.000000 | 0.000000

And when using m.train_component_cols.T.dot (np.array (m.params ['beta']) [0]) I get the following:

component
A 0.000248
B -0.002786
C 0.000000
D 0.000000

Do I understand correctly that in the second case the coefficients are standardized, but in the first case they are no longer?

When I do forecast = m.predict (future) I get:

A | A_lower | A_upper | B | B_lower | B_upper
-0,24199 | -0,24199 | -0,24199 | 2,076534 | 2,076534 | 2,076534

What is it? Are these the contributions of the regressors to the result? Then why the regressor A with a positive coefficient gives a negative contribution, I do not understand, tell me, please. Sorry for bad english)