Abstract: In some embodiments, a plurality of devices may be respectively mapped to a plurality of interaction types. Statistical models may be generated, where the statistical models are indicative of relationships between aggregated interaction data related to the interaction types and aggregated resource utilization data related to the devices. With respect to each interaction type of the interaction types, the statistical models may be scored based on strength of one or more of the relationships. A subset of the statistical models may be selected based on the scoring for the interaction types. As an example, a first statistical model is included in the subset based on the scoring related to a first interaction type and a second statistical model is included in the subset based on the scoring related to a second interaction type different from the first interaction type. One or more of the devices may be remapped to one or more interaction types based on the selected subset of the statistical models.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.