Embodiments of the present invention provide a method for detecting a temporal change of name associated with performance data. The method comprises receiving at least one candidate name replacement pair comprising a pair of names. The method further comprises, in a training stage, for each known name replacement pair included in the performance data, determining a window of time covering a most recent appearance of a first name of the known name replacement pair. The window of time is determined based on quantitative features of a time series model comprising performance data for the first name and a second name of the known name replacement pair. The method further comprises, in the training stage, training a machine learning classifier based on quantitative features computed using a portion of the performance data for the first name and the second name, where the portion is within the window of time determined.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for detecting a temporal change of name associated with performance data, comprising: receiving at least one candidate name replacement pair, wherein each name replacement candidate pair comprises a pair of names; and in a training stage: for each known name replacement pair included in the performance data: based on quantitative features of a time series model comprising performance data for a first name and a second name of the known name replacement pair, determining a window of time for the known replacement pair that covers a most recent appearance of the first name of the known name replacement pair, wherein the known name replacement pair is one of a positive example of a name replacement or a negative example of a name replacement; and training a machine learning classifier, executing on a hardware processor, based on quantitative features computed using a portion of the performance data for the first name and the second name of the known name replacement pair, wherein the portion of the performance data for the first name and the second name of the known name replacement pair is within the window of time determined for the known replacement pair. 2. The method of claim 1, further comprising: in a classification stage: for each candidate name replacement pair: based on quantitative features of a time series model comprising performance data for a first name and a second name of the candidate name replacement pair, determining a window of time for the candidate name replacement pair that covers a most recent appearance of the first name of the candidate name replacement pair; and applying the trained classifier to classify the candidate name replacement pair based on quantitative features computed using a portion of the performance data for the first name and the second name of the candidate name replacement pair, wherein the portion of the performance data for the first name and the second name of the candidate name replacement pair is within the window of time determined for the candidate name replacement pair. 3. The method of claim 2, wherein the quantitative features computed in the classification stage are combined from features computed from instances of possible name changes determined at more than one level of data aggregation. 4. The method of claim 1, wherein a window of time for a name replacement pair is determined by searching for peak data values or trough data values closest to a most recent appearance of a first name of the name replacement pair. 5. The method of claim 1, further comprising: generating a candidate name replacement pair that is a positive synthetic example of a name replacement. 6. The method of claim 5, wherein generating a candidate name replacement pair that is a positive synthetic example of a name replacement comprises: randomly selecting a point along a time series model comprising performance data for a single name only; and generating a candidate name replacement pair based on the selected point, wherein the candidate name replacement pair generated includes a first name that covers a portion of the time series model including the selected point and points occurring before the selected point, and a second name that covers a remaining portion of the time series model including points occurring after the selected point. 7. The method of claim 1, further comprising: generating a candidate name replacement pair that is a negative synthetic example of a name replacement. 8. The method of claim 7, wherein generating a candidate name replacement pair that is a negative synthetic example of a name replacement comprises: generating a candidate name replacement pair based on a first positive known example of a name replacement and a second positive known example of a name replacement; wherein the first positive known example represents a first name replaced over time by a second name; wherein the second positive known example represents a third name replaced over time by a fourth name; wherein the first and the second positive known examples differ in a pre-specified manner; wherein a most recent appearance of the first name of the first positive known example occurs at the same time as a most recent appearance of the third name of the second positive known example; and wherein the candidate name replacement pair generated includes a part of the first positive known example that includes the first name and a part of the second positive known example that includes the fourth name. 9. A system comprising: a memory having computer readable computer instructions; and a processor for executing the computer readable instructions, the instructions including: receiving at least one candidate name replacement pair, wherein each name replacement candidate pair comprises a pair of names; and in a training stage: for each known name replacement pair included in the performance data: based on quantitative features of a time series model comprising performance data for a first name and a second name of the known name replacement pair, determining a window of time for the known replacement pair that covers a most recent appearance of the first name of the known name replacement pair, wherein the known name replacement pair is one of a positive example of a name replacement or a negative example of a name replacement; and training a machine learning classifier, executing on a hardware processor, based on quantitative features computed using a portion of the performance data for the first name and the second name of the known name replacement pair, wherein the portion of the performance data for the first name and the second name of the known name replacement pair is within the window of time determined for the known replacement pair. 10. The system of claim 9, the instructions further including: in a classification stage: for each candidate name replacement pair: based on quantitative features of a time series model comprising performance data for a first name and a second name of the candidate name replacement pair, determining a window of time for the candidate name replacement pair that covers a most recent appearance of the first name of the candidate name replacement pair; and applying the trained classifier to classify the candidate name replacement pair based on quantitative features computed using a portion of the performance data for the first name and the second name of the candidate name replacement pair, wherein the portion of the performance data for the first name and the second name of the candidate name replacement pair is within the window of time determined for the candidate name replacement pair. 11. The system of claim 9, wherein the quantitative features computed in the classification stage are combined from features computed from instances of possible name changes determined at more than one level of data aggregation. 12. The system of claim 9, wherein a window of time for a name replacement pair is determined by searching for peak data values or trough data values closest to a most recent appearance of a first name of the name replacement pair. 13. The system of claim 9, the instructions further including: generating a candidate name replacement pair that is a positive synthetic example of a name replacement. 14. The system of claim 13, wherein generating a candidate name replacement pair that is a positive synthetic example of a name replacement comprises: randomly selecting a point along a time series model comprising performance data for a single name only; and generating a candidate name replacement pair based on the selected point, wherein the candidate name replacement pair generated includes a first name that covers a portion of the time series model including the selected point and points occurring before the selected point, and a second name that covers a remaining portion of the time series model including points occurring after the selected point. 15. The system of claim 9, the instructions further including: generating a candidate name replacement pair that is a negative synthetic example of a name replacement. 16. The system of claim 15, wherein generating a candidate name replacement pair that is a negative synthetic example of a name replacement comprises: generating a candidate name replacement pair based on a first positive known example of a name replacement and a second positive known example of a name replacement; wherein the first positive known example represents a first name replaced over time by a second name; wherein the second positive known example represents a third name replaced over time by a fourth name; wherein the first and the second positive known examples differ in a pre-specified manner; wherein a most recent appearance of the first name of the first positive known example occurs at the same time as a most recent appearance of the third name of the second positive known example; and wherein the candidate name replacement pair generated includes a part of the first positive known example that includes the first name and a part of the second positive known example that includes the fourth name. 17. A computer program product comprising a computer readable storage medium having program code embodied therewith, the program code is executable by a processor to: receive at least one candidate name replacement pair, wherein each name replacement candidate pair comprises a pair of names; and in a training stage: for each known name replacement pair included in the performance data: based on quantitative features of a time series model comprising performance data for a first name and a second name of the known name replacement pair, determine a window of time for the known replacement pair that covers a most recent appearance of the first name of the known name replacement pair, wherein the known name replacement pair is one of a positive example of a name replacement or a negative example of a name replacement; and train a machine learning classifier, executing on a hardware processor, based on quantitative features computed using a portion of the performance data for the first name and the second name of the known name replacement pair, wherein the portion of the performance data for the first name and the second name of the known name replacement pair is within the window of time determined for the known replacement pair. 18. The program product of claim 17, the program code further executable by the processor to: in a classification stage: for each candidate name replacement pair: based on quantitative features of a time series model comprising performance data for a first name and a second name of the candidate name replacement pair, determine a window of time for the candidate name replacement pair that covers a most recent appearance of the first name of the candidate name replacement pair; and apply the trained classifier to classify the candidate name replacement pair based on quantitative features computed using a portion of the performance data for the first name and the second name of the candidate name replacement pair, wherein the portion of the performance data for the first name and the second name of the candidate name replacement pair is within the window of time determined for the candidate name replacement pair. 19. The program product of claim 17, wherein the quantitative features computed in the classification stage are combined from features computed from instances of possible name changes determined at more than one level of data aggregation. 20. The program product of claim 17, wherein a window of time for a name replacement pair is determined by searching for peak data values or trough data values closest to a most recent appearance of a first name of the name replacement pair.