An example method includes receiving a data set, generating a topological representation using topological data analysis, at least one metric-lens combination, and the data set, the representation including a plurality of nodes, each of the nodes having one or more data points as members, receiving a new data point, determining distances between the new data point and at least some of the one or more data points, locating the new data point in a location relative to one or more of the nodes using the distances, identifying a subset of the data points closest to the location of the new data point, comparing the subset of the data points to at least some information regarding the new data point to identify a regime, and generating a report indicating a model associating factors associated with the subset of the data points with the new data point for predicting future outcomes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer readable medium including executable instructions, the instructions being executable by a processor to perform a method, the method comprising: receiving a data set; generating a topological representation using the received data set and topological data analysis, the topological representation being generated using at least one metric-lens combination of a subset of metric-lens combinations, the topological representation including a plurality of nodes, each of the nodes having one or more data points from the data set as members, at least two nodes of the plurality of nodes being connected by an edge if the at least two nodes share at least one data point from the data set as members; receiving a new data point; determining distances between the new data point and at least some of the one or more data points from the data set; locating the new data point in a location relative to one or more of the nodes in the topological representation using the distances between the new data point and the at least some of the one or more data points from the data set; identifying a subset of the data points closest to the location of the new data point; comparing the subset of the data points to at least some information regarding the new data point to identify a regime associated with the new data point; and generating a report indicating a model associating factors associated with the subset of the data points with the new data point for predicting future outcomes. 2. The non-transitory computer readable medium of claim 1 wherein the topological representation is a visualization depicting the plurality of nodes and the edge. 3. The non-transitory computer readable medium of claim 1 wherein each of the one or more data points from the data set are identified by a date indicating a plurality of conditions associated with that date. 4. The non-transitory computer readable medium of claim 3 wherein the new data point is associated with a new date and the subset of the data points closests to the location for the new data point include at least one similar condition of the plurality of conditions. 5. The non-transitory computer readable medium of claim 3 wherein the model predicts an outcome associated with information regarding the new data point when the least one similar condition of the plurality of conditions recurs. 6. The non-transitory computer readable medium of claim 1 wherein the distances between the new data point and the at least some of the one or more data points from the data set is based on a metric from the at least one metric-lens combination. 7. The non-transitory computer readable medium of claim 1 wherein the distances between the new data point and the at least some of the one or more data points from the data set is based on a graphical distance of the topological representation. 8. The non-transitory computer readable medium of claim 1 wherein a number of the subset of the data points closest to the new data point is based on a proximity value. 9. The non-transitory computer readable medium of claim 8 wherein the proximity value is received from a digital device. 10. The non-transitory computer readable medium of claim 1 wherein information associated with the new data point and the number of the subset of the data points closest to the new data point is based on a proximity value is analyzed using statistical measures to determine correlations. 11. A method comprising: receiving a data set; generating a topological representation using the received data set and topological data analysis, the topological representation being generated using at least one metric-lens combination of a subset of metric-lens combinations, the topological representation including a plurality of nodes, each of the nodes having one or more data points from the data set as members, at least two nodes of the plurality of nodes being connected by an edge if the at least two nodes share at least one data point from the data set as members; receiving a new data point; determining distances between the new data point and at least some of the one or more data points from the data set; locating the new data point in a location relative to one or more of the nodes in the topological representation using the distances between the new data point and the at least some of the one or more data points from the data set; identifying a subset of the data points closest to the location of the new data point; comparing the subset of the data points to at least some information regarding the new data point to identify a regime associated with the new data point; and generating a report indicating a model associating factors associated with the subset of the data points with the new data point for predicting future outcomes. 12. The method of claim 11 wherein the topological representation is a visualization depicting the plurality of nodes and the edge. 13. The method of claim 11 wherein each of the one or more data points from the data set are identified by a date indicating a plurality of conditions associated with that date. 14. The method of claim 13 wherein the new data point is associated with a new date and the subset of the data points closests to the location for the new data point include at least one similar condition of the plurality of conditions. 15. The method of claim 13 wherein the model predicts an outcome associated with information regarding the new data point when the least one similar condition of the plurality of conditions recurs. 16. The method of claim 1 wherein the distances between the new data point and the at least some of the one or more data points from the data set is based on a metric from the at least one metric-lens combination. 17. The method of claim 1 wherein the distances between the new data point and the at least some of the one or more data points from the data set is based on a graphical distance of the topological representation. 18. The method of claim 1 wherein a number of the subset of the data points closest to the new data point is based on a proximity value. 19. The method of claim 8 wherein the proximity value is received from a digital device. 20. The method of claim 1 wherein information associated with the new data point and the number of the subset of the data points closest to the new data point is based on a proximity value is analyzed using statistical measures to determine correlations. 21. A system comprising: a processor; a memory including instructions to configure the processor to: receive a data set; generate a topological representation using the received data set and topological data analysis, the topological representation being generated using at least one metric-lens combination of a subset of metric-lens combinations, the topological representation including a plurality of nodes, each of the nodes having one or more data points from the data set as members, at least two nodes of the plurality of nodes being connected by an edge if the at least two nodes share at least one data point from the data set as members; receive a new data point; determining distances between the new data point and at least some of the one or more data points from the data set; locate the new data point in a location relative to one or more of the nodes in the topological representation using the distances between the new data point and the at least some of the one or more data points from the data set; identify a subset of the data points closest to the location of the new data point; compare the subset of the data points to at least some information regarding the new data point to identify a regime associated with the new data point; and generate a report indicating a model associating factors associated with the subset of the data points with the new data point for predicting future outcomes.