| 摘要 |
This disclosure relates generally to system modeling, and more particularly to systems and methods for modeling computer resource metrics. In one embodiment, a processor-implemented computer resource metric modeling method is disclosed. The method may include detecting one or more statistical trends in aggregated interaction data for one or more interaction types, and mapping each interaction type to one or more devices facilitating the transactions. The method may further include generating one or more linear regression models of a relationship between device utilization and interaction volume, and calculating one or more diagnostic statistics for the one or more linear regression models. A subset of the linear regression models may be filtered out based on the one or more diagnostic statistics. One or more forecasts may be generated using the remaining linear regression models, using which a report may be generated and provided. |
| 主权项 |
1. A processor-implemented computer resource metric modeling method, comprising:
aggregating, via one or more hardware processors, interaction data related to one or more interaction types; detecting, via the one or more hardware processors, one or more statistical trends in the interaction data for each interaction type; mapping, via the one or more hardware processors, for each interaction type of the business transaction types, one or more devices facilitating the interaction type; aggregating, via the one or more hardware processors, device utilization data for the one or more devices; detecting, via the one or more hardware processors, one or more statistical trends in the device utilization data; generating, via the one or more hardware processors, one or more linear regression models of a relationship between device utilization and interaction volume; calculating, via the one or more hardware processors, one or more diagnostic statistics for the one or more linear regression models; filtering out, via the one or more hardware processors, a subset of the linear regression models based on the one or more diagnostic statistics; generating, via the one or more hardware processors, one or more forecasts using the remaining linear regression models; and generating and providing, via the one or more hardware processors, a report based on the one or more forecasts. |
