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I would like to forecast parking space occupancy for several parking lots. My dataset contains information on the date and time of entering/exiting cars, parking capacity and price.

Is it possible to use time series modeling to predict when occupancy rates of the lots will increase/decrease throughout the day?

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A time series at the very least requires a time measurement, as well as measuring a dependent variable that changes as time passes. In your case, your variable of interest would be the occupancy rate.

You would need to reorganize the dataset into something like the following:

 Time    # of Cars in Parking Lot    Parking Capacity    % Parking Capacity Filled     Price
 7:00          10                              100               10                       15

Then you would have a table of each time stamp and its respective measurement of occupancy rate, which is a time series. Then you can use time series modelling. So yes, it is possible.

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