To forecast the values of future time steps of a sequence, you can train a sequence-to-sequence regression LSTM network, where the responses are the training sequences with values shifted by one time step. That is, at each time step of the input sequence, the LSTM network learns to predict the value of the next time step.
Please refer to the attached example, "TimeSeriesForecastLSTM.mlx", which demonstrates how to forecast time-series data using a long short-term memory (LSTM) network.
This example trains an LSTM network to forecast the number of chickenpox cases given the number of cases in previous months. The training data contains a single time series, with time steps corresponding to months and values corresponding to the number of cases.
Further, you mentioned that you need to forecast the values for the last 10 steps. To forecast the values of multiple time steps in the future, you can use the "predictAndUpdateState" function to predict time steps one at a time and update the network state at each prediction. Please refer to the documentation of the "predictAndUpdateState" function for more information on how to use the function by typing the following command in the Command Window:
>> doc predictAndUpdateState