Method, apparatus, and computer program product for balancing network resource demand
Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting network asset requests for a future network time interval.
1. A system for predictively balancing network resource demand, the system comprising:
a network resource demand balancing server coupled with a communication network, the network resource demand balancing server having a processor, the processor, when executing computer-readable instructions, is configured to:
generate a first prediction value that indicates a programmatically expected number of network asset search requests for a future network time interval, each network asset search request associated with a network asset provider group, each network asset provider group associated with a group location and a group network asset type;
select a network asset provider record that includes a network asset provider location and a network asset type; and
generate a second prediction value for the network asset provider record that indicates a programmatically expected number of network asset transactions for the future network time interval.
2. The system of claim 1 , wherein the first prediction value is based on a machine learning model.
3. The system of claim 2 , wherein the machine learning model comprises a random forest model.
4. The system of claim 2 , wherein the machine learning model is trained with historical data.
5. The system of claim 4 , wherein the historical data comprises historical network asset search request data.
6. The system of claim 1 , wherein the second prediction value is based on a machine learning model.
7. The system of claim 1 , wherein each of the network asset provider location and the group location comprises GPS coordinates.
8. The system of claim 1 , wherein a network asset search request is a request for a device rendered objects.
9. The system of claim 8 , wherein each device rendered object comprises one or more object attributes and an object location.
10. The system of claim 9 , wherein an object attribute of the one or more object attributes is an asset type.
11. The system of claim 10 , wherein the object location comprises GPS coordinates.
12. The system of claim 1 , wherein the processor is further configured to transmit device rendered objects to a remote network asset requester device based on the second prediction value.
13. An apparatus for predictively balancing network resource demand, the apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to:
generate a first prediction value that indicates a programmatically expected number of network asset search requests for a future network time interval, each network asset search request associated with a network asset provider group, each network asset provider group associated with a group location and a group network asset type;
select a network asset provider record that includes a network asset provider location and a network asset type; and
generate a second prediction value for the network asset provider record that indicates a programmatically expected number of network asset transactions for the future network time interval.
14. The apparatus of claim 13 , wherein one or more of the first prediction value and the second prediction value is based on a machine learning model.
15. The apparatus of claim 14 , wherein the machine learning model comprises a random forest model that is trained with historical data.
16. The apparatus of claim 13 , wherein the first prediction value is further based on historical network asset search request data.
17. The apparatus of claim 13 , wherein each of the network asset provider location and group location comprises GPS coordinates.
18. The apparatus of claim 13 , wherein the network asset search requests are requests for device rendered objects.
19. The apparatus of claim 18 , wherein each device rendered object comprises one or more object attributes and an object location.
20. The apparatus of claim 19 , wherein an object attribute of the one or more object attributes is an asset type.
21. The apparatus of claim 20 , wherein the object location comprises GPS coordinates.
22. The apparatus of claim 13 , wherein the processor is further configured to transmit device rendered objects to a remote network asset requester device based on the second prediction value.