Vector representation in multidimentional time-ser

问题:

I have a large data set (~30 million data-points with 5 features) that I have reduced using K-means down to 200,000 clusters. The data is a time-series with ~150,000 time-steps. The data on which I would like to train the model is the presence of particular clusters at each time-step. The purpose of the predictive model is generate a generalized sequence similar to generating syntactically correct sentences from a model trained on word sequences. The easiest way to think about this data is that I'm trying to predict the pixels in the next video frame from pixels in the current video frame in order to generate a new sequence of frames that approximate the original sequence.

The raw and sparse representation at each time-step would be 200,000 binary values representing which clusters are present or not at that time step. Note, no more than 200 clusters may be present in any one time-step and thus this representation is extremely sparse.

What is the best representation to convert this sparse vector to a dense vector that would be more suitable to time-series prediction using Tensorflow?

I initially had in mind a RNN / LSTM trained on the vectors at each time-step, but due to the size of the training vector I'm now wondering if a convolution approach would be more suitable.

Note, I have not actually used tensorflow beyond some simple tutorials, but have have previously used OpenCV ML functions. Please consider me a novice in your responses.

Thank you.