Consider a dataset of thousands of car repairs that have been performed. In simplest of terms, the columns to consider are the time of year when it was broken (seasonal changes in demand for car repairs), type of damage to car (some damages take longer to repair than others), and the type of car (some cars are more difficult to work on).
I am inquiring as to the best fit for trying to model data of this format where you are predicting the repair time based off of timeseries and categorical data as the inputs. Keep in mind that the data does not have a constant period.
Example Column Names:
datetime | Type of Damage to Car | Type of Car | Repair Time