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I am currently working on a multivariate LSTM model to forecast stock prices and am getting confused about how this model works.

For univariate, it is straight forward. I have a dataset with only one feature (e.g Closing price) and feed it into the model and it will output one result. To forecast beyond available data, I then perform a loop to forecast $n$ days - assuming time-step is 30 days, I will take the last 30 days to forecast for the first day closing price. To forecast the second day, I will take the last 29 days + 1 forecast value (from first day) to forecast and this repeats for n iteration.

My confusion comes in at multivariate. For multivariate, assuming I have a dataset with 5 features.

  1. I feed 5 features into the model, and the output should be 5 features?
  2. If point 1 is holds, then when I perform forecast beyond available data, will it be the same exact strategy as the univariate?
  3. If my end goal is to forecast Closing price in multivariate, then how should I approach to do this? Is it still right for me to perform point 2 and only get the Closing price element in the result array?

Thank you.

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