Using different timesteps for features and target value 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(using the predicted value as a new feature)

Will these 2 approaches give similar results?
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:


Use LSTM as it handles time series better
You can predict 3 consecutive numbers from an RNN as the next three days' predictions
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
Don't forget normalization