I would like to know whether it's wrong; when working with time series data; to use daily prices as features and the price after 3 days as target. Is this correct or should I use the next-day price as target and after training; predict 3 times; each time for one more day ahead(to get the price after 3 days) Will these 2 approaches give similar results?

I don't know whether there is any difference but I am also interested about how(if) the answer to the above question changes when using :

1) LSTM vs NN

2) Regression vs Classification(e.g. price will go up/down)

I don't know what kind of price data you're dealing with. I suppose the order of the data matters a lot, so my suggestion would be:

  1. Use LSTM as it handles time series better

  2. You can predict 3 consecutive numbers from an RNN as the next three days' predictions

  3. Try regression first, it is likely it will not work (or just flatten the curves, depend on your data noise), then classification is an easier approach

  4. Don't forget normalization

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