Regression - predict n numbers based on another n numbers I'd like to get a recommendation how to attack a problem of predicting multiple numbers.
Training data contains 4 columns, each says a probability of record being in the bucket. So, for example:
X = [0.25, 0.5, 0.25, 0.0]
and
corresponding output should be e.g. Y = [0.8, 0.1, 0.0, 0.1].
Each row should sum to 1.0

What type of approach would you recommend?

I already tried simple neural network with 4 neurons in the last layer and softmax activation but wondering if there is a better solution.

Thanks!
I would recommend using an encoder-decoder structure based on LSTM-RNN for sequence-to-sequence learning; it will provide you with exactly what you want. A link that I find very helpful is:

https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/