How to correctly configure Encoder Decoder LSTM to have one timestep output carrying multiple features

Question

In each observation, I have 6 timesteps each with 2 features, and I am trying to predict 1 timetsep that has 2 parallel features. More specifically,

The shape of my input data is: (81, 6, 2) The shape of my output data is: (81, 1, 2)

I wrote the following code to build Encoder-Decoder LSTM:

model.add(LSTM(200, activation='relu', input_shape=(n_input, 2)))
model.add(RepeatVector(1))
model.add(LSTM(200, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(100, activation='relu')))
model.add(TimeDistributed(Dense(2)))

The network gives me back the shape (1, 1, 2) when I perform a single prediction.

I want to double check if this is correct, and I am not missing anything, because the predicted values are very bad (some are negative and others are very high).