title: Shape of data and LSTM Input for varying timesteps
body: <p>For my master thesis, I want to predict the price of a stock in the next hour using a LSTM model. My X data contains 30.000 rows with 6 dimensions (= 6 features), my Y data contains 30.000 rows and only 1 dimension (=target variable). For my first LSTM model, I reshaped the X data to (30.000x1x6), the Y data to (30.000x1) and determined the input like this:
  input_nn = Input(shape=(1, 6))</p>
  
  <p>I am not sure how to reshape the data and to determine the input shape for the model if I want to increase the timesteps. I still want to predict the stock price in the next hour, but include more previous time steps. 
  Do I have to add the data from previous timesteps in my X data in the second dimension? </p>
  
  <p>Can you explain what the number of units of a LSTM exactly refers to? Should it be the same as the number of timesteps in my case?</p>