Suppose I want to create a RNN-model that will learn to predict 24 hours into the future, given 24 hours of the past. Traditionally, if I want to create such a multistep model, I would have an input like a time series (24 hours long) with one feature e.g. temperature. What if I regard each time step of the 24 hours in the time series as an individual feature so that I have 24 features with one input to predict 24 features with one output. Would such a model be superior to a traditional model?

CodePudding user response：

If you always have a fixed number of inputs (like 24 hours of temperatures) it might be a good way to not use a RNN but instead go with a traditional feed forward structure. The main reason why one uses RNNs is that they can handle time series of variable length, so for all applications that need this property they are pretty much the only choice. In your case, a feed forward architecture will probably work fine, but it should not be that difficult to just try out both.