We have hourly time-series data of the status of a system: number of people present at different train stations. We collected it for a year, and we want to use it to train a model to predict the status of this system in future.
We know that several parameters influence the system status, such as: temperature, type of day (holiday/working day/weekend), period of the year, etc. Thus, we collected data for these parameters in parallel to the data of our system status.
We are interested in implementing a tool that predicts the status of the system in the next hours based on the previous observations of the system status and on the external parameters that affect it.
What methods are most suitable for such a problem?