Question on regression (AR model)?

I have some historical data RV that I want to fit a model to. The model is:

RV(t+1) = C0 + C1*RV(t) + C2*RV_weekAverage(t-5) + C3*RV_monthAverage(t-30) + e

where the future RV depends on the previous RV and averages of some previous RV values.

t - is time

C0, C1, C2, C3 - are parameters to be determined

RV_weekAverage = (1/5)*(sum of RVs from t-1 to t-5)

RV_monthAverage = (1/30)*(sum of RVs from t-1 to t-30)

e - error

I think I am supposed to use an AR model but I am not exactly sure how to implement it since for an AR model the right-hand side don't have averages but simple previous values such as:

RV(t+1) = C0 + C1*RV(t) + C2*RV(t-1) + e

To use the AR model, I would have to try:

RV(t+1) = C0 + C1*RV(t) + C2*(1/5)*[RV(t-1)+...+RV(t-5)] + C3*(1/30)*[RV(t-1)+...+RV(t-30)] + e

I am not sure how to include the factor of (1/5) or (1/30) into the model without interfering with the parameters C0, C1, C2, and C3 when I try to estimate them. This is all I have so far:

model = arima(6,0,0)
fit = estimate(model,RV)