I am trying to predict customer spending for an X year period after t0. I train an svm model with transactions occurring before and on t0, on the cumulative spending of the customers after t0. I then use the model to make predictions on a test data set to assess the models accuracy. The test dataset is from 1 period (in the example below 1 day) in the future. How does an svm deal with newly acquired customers?

Question elaboration:

My training data is structured as follows:

>DT1
   Id t.-3 t.-2 t.-1 t.0 Target.spending
1:  1   29   25   14  25             100
2:  2   NA   30    0   0               0
3:  3   NA   16    0  13               0
4:  4   NA   NA   62  18               5

Customer 1 was acquired on t.-3 when he purchased worth 29$. His second purchase occurred on t.-2. etc... Customer 2 was acquired on t.-2 and only purchased on that day. Target.spending is the cumulative spending that occurred in the next four days (i.e. t.1+t.2+t.3+t.4).

Then, I predict the spending of the next four days using an svm:

train.model <- svm(Target.spending ~ . , data = DT)

Using a test data set from one day in the future (where t.0 (DT1) = t.-1 (DT2)), I will predict the target.spending (DT2) and assess the model accuracy. In the test data, on t.0 (DT2), customer 5 was acquired.

>DT2
   Id t.-3 t.-2 t.-1 t.0 Target.spending
1:  1   25   14   25  10             103
2:  2   30    0    0   0               0
3:  3   16    0   13   0               0
4:  4   NA   62   18   4               1
5:  5   NA   NA   NA   9              30

How does the svm deal with new customer acquisitions?