I have built an LSTM in TensorFlow that is unrolled into n timesteps and trains successfully on sequences of lengths n.
I would now like to use my trained model to make m daily predictions one by one. One way of doing this is that for each of the m daily predictions, I would take a sliding window of length n, with the final timestep taking in the input data for this day's prediction. Each day, this window of length n is slid over by one day.
Another way I have seen is to use an LSTM unrolled into just a single timestep for these predictions. The previous n (or more or less) datapoints are passed through the single timestep LSTM one at a time to "warm up" its hidden state. Then, for each of the m prediction days, just the input data for this day is fed into the LSTM and a prediction is produced from this input and from the hidden state.
Is it possible to train an LSTM in TensorFlow which is unrolled into n timesteps, and then use a single timestep LSTM with the trained parameters to make predictions? My idea would be to train the unrolled model, save the parameters, initialize a single timestep LSTM, and populate its parameter with the stored ones. Can this be done?