autoEncoder as LSTM input, any benefit?
The purpose of the autencoder would be similar to doing an embedding of text to pass latent space features of the input to the LSTM, as opposed to raw inputs. In the case of text, this is usually done to reduce the very large and sparse dictionary (word subword, whatever) space to a   space which is less sparse, significantly smaller, and therefore more computationally efficient. 

In your case, it looks like the data is not high dimensional to start with (I am assuming this because of the significant events over time), so you would not get the benefit of mapping the input to a latent space. If your trend over time is high dimensional, then it may be worth it, as the embedding/autencoding may pick up and reduce correlations between trends in a few dimensions.