Deep Neural Network in Python with 3 inputs and 3 outputs
I would like to implement a deep neural network in Python (preferably PyTorch, but TensorFlow is also possible) which predicts the next location and the time of the arrival at that location. For the raw data I have a csv file with a sequence of three values: latitude, longitude, and time:
39.984702,116.318417,2008-10-23,02:53:04
39.984683,116.31845,2008-10-23,02:53:10
39.984686,116.318417,2008-10-23,02:53:15
...

The number of such rows is around 100 000. So, here is my question. How should I split the data, normalize it and transform it, in order to feed it into the DNN (preferably GRU or LSTM, but as I read CNN are also possible) and receive as an output a predicted location and time of arrival?
Based on my current research, what should be done is to split the data into sequences (of n length), normalize the values, maybe even change the format of the time (for sure not feeding it as a string), and treat the last value in the sequence as a label during the teaching of the DNN.
A simple code would be really helpful, with my problems of understanding the different dimensions of the input and outputs for the NNs.

Just a tip, for the time I would transform it into an Epoch Unix Timestamp.
