Relation mining of multivariant categorical timeseries without excluding the temporal nature To all:

I have been wracking my brain at this for a while and thought maybe someone here would know of a package or algorithm to handle the following:

I have nominal multivariant timeseries that look like the following:

          Time Var1 Var2 Var3 Var4 Var5 ... VarN
             0     A     A   B    C    A   ... H
             1     A     A   B    D    D   ... H
             2     B     A   C    D    D   ... H
             ..


And so on from times 0 to 1,000,000. What I would like to do is search the time series for rules of the type:

Given Var3 is in state B in the previous step and Var5 is in state D in the previous step, than Var1 will be in state B. What I want to do is have the rules that include the time interval explicitly. A simpler case of interest would simply be to reduce the time series to 

               Time    Var1 Var2 Var3 Var4 Var5 ... VarN
                0        0    0    0     0   0   ... 0
                1        0    0    0     1   1   ... 0
                2        1    0    1     0   0   ... 0


Where the the variable is 1 if its state is different from the previous step and zero otherwise. Then I just want to have rules that say something like:

If Var4 and Var5 changed in the previous step than Var1 will change in the current step. Which would be easy for a lag of one, as I could just make the data into something like:

   Var1 Var2 Var3 Var4 Var5 ... VarN Var1_t-1 Var2_t-1 Var3_t-1 ...


and then do sequence mining, but if I want to have rules that aren't just a single lag but could be lags from 1 to 500 than my data set begins to be a little difficult to work with. 

Any help would be greatly appreciated. 

Edit to respond to comment:
Each column could be in one of 7 different states. As far as a target, it is non-specific, any rules between the columns would be of interest. However, predicting columns 30-40 and 62-75 would be particularly interesting.
This problem is one of estimating the lag. Once that is estimated, you could create additional features representing the lagged values and move forward with "sequence mining" as you have already suggested in the question itself. 

For each variable, Var_i, you will have to estimate its lag l_i. This lag can be calculated by estimating the order of a Markov chain with seven symbols (you could use either BIC or AIC to estimate this order; both would require calculating likelihood of candidate orders and pick the order that maximizes either of these criteria). Once you are done calculating the order of the Markov chain for each of the variables, then you could represent your dataset such that each row will have the current value of Var_i and its preceding values, all the way back to its estimated lag l_i. While this methodology is laborious, it pays rich dividends as its automated and parsimonious way of representing the necessary information.