I have two time series that follow a random walk behaviour. I would like to use the one I know to forecast the other one.

Suppose there is a strong correlation between them with $text{lag}=k$, for example. This lag would permit me to forecast one because I have the information $k$ days before from the other one, even if it is a random walk.

Does it make sense? The problem I intuitively observe is that I'm trying to forecast a random evolution.

**Contents**hide

#### Best Answer

I've finally used a **VAR(q)** model. It fits very well my goal, in effect my two time series are interdependent and I can use one to forecast the other one.

When I've build my model with R, I had to specify my two time series and to find the good q value. AIC and BIC criterions are good to do that.