# Can we say that RNN for time series is an example of semi-supervised learning?

I am learning neural nets, esp. focusing on RNN for my research problem. This question has nothing exactly to do with my research.

With my understanding of RNN, I can think of it as an example of semi-supervised learning rather than supervised learning. It is because we do not have an exact data set (unsupervised, since no actual labels), but we use the shifted value of the input as the data set (makeshift labels). Hence this makes RNN a semi-supervised learning algorithm (at least for time series).

Am I correct in this understanding?

The most "classic" use of RNNs is in language modeling, where we model $$p(x) = \prod_i p(x_{i} | x_{j, and each conditional factor is computed by the RNN. This is an unsupervised model.