Nominal Encoding techniques for high cardinality categorical variables in time series analysis
One Idea: use the word embeddings of the corresponding cities from a Wikipedia word2vec model.
My Time series data has a feature similar to Zipcode with 300+ unique codes. How to encode this high cardinality categorical feature which has no inherent order. I am trying to train a time series model which has data points with repeated zip codes and doesn't want to lose any information.
I've gone through mean/Target encoding but it has some disadvantages like overfitting etc. Are there any other encoding techniques apart from that exclusively for time series data?