User Location Prediction
KNN:

The prediction of Lat/Lng from a given timestep could be performed by analysing the previous data of the user. This is a totally naive approach but could be tried,


Take an input timestamp from the user. Let's call it $x$.
From the previous data of the user, search for the timestamp which is the closest to $x$.
Once you find the closest timestamp, output the Lat/Lng associated with that timestamp.



  This approach is called K-NN ( K Nearest Neighbours ) classification.
  It will work well if the user is mostly at a common place at a given
  time ( timestamp ).


Clustering method:

This method will work on making clusters of Lat/Lng. For some a given time-interval, suppose, you will get a common timestep ( which will be the centre of the cluster ).


Once you have a cluster of timesteps, find the timestep ( centre of the cluster ) which is the closest to the input $x$.
For this timestep, you will get Lat/Lng which close to one another.
For predicting the Lat/Lng, use the centre of the cluster and output the associated Lat/Lng values.



  Basically, you are clustering similar data points which help in prediction. You can find where a user's location is clustered at given time intervals.