I have to predict the next step(s) in a multivariate time series with about 30 features and 50.000 samples. I am thinking of using LSTM. Which techniques are usually recommended for cleaning the data when using LSTM?
Does it make sense to transform the data into a stationary time series when using LSTM? Should the data be normally distributed when you are using PCA?
There is also a very large amount of missing timestamps. Does it make sense to impute/fill (by forward filling or something else) big gaps or is it just better in that case to ignore the missing data completely?