I'm trying to create an algorithm (neural network) that is able to predict a time series from a set of different parameters that are not given through time. Let's say I have a plane flying under the following conditions:
|Angle of attack||8 degrees|
|Lateral angle||12 degrees|
|Wind speed||-20 m/s|
|Plane speed||200 m/s|
From this point, I would like to predict the translational velocities in x-y-z axis for the next 2-3 seconds.
In order to train my model, I have a data base with different initial situations (input) and different motions of the plane (desired output) linked to their initial situation. Therefore, I want to train my model to predict these motions mentioned before, based only on the initial situation described.
In other words, the basics of what I'm trying to do could be summed up as the following:
Parameters describing the initial situation -> Model -> Time series of translational velocities.