Is it meaningful to use word2vec for non-string inputs like time series analysis?
The goal of Word2vec is to represent each element of a sequence into an "embedded space", i.e. a lower dimensional space where "similar" elements are located closer to each other.

I think this is a bit off topic for time series analysis. If you want to detect outliers, you might try looking at the normalized distance of each datapoint from the trend, and set a threshold.

Alternatively, if you really want to stick with Neural Networks, you can use Autoencoders. They can be applied to outlier detection, but it's not the simplest model to implement.