A method and system to identify a time lagged indicator of an event to be predicted are described. The method includes receiving information including an indication of a factor, the factor being a different event than the event to be predicted, and identifying a window period within which the event is statistically correlated with the factor. The method also includes collecting data for a duration of the window period, the data indicating occurrences of the factor and the event, and identifying a time lagged dependency of the event on the factor based on analyzing the data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of identifying a time lagged indicator of an event to be predicted, the method comprising: receiving information including an indication of a factor, the factor being a different event than the event to be predicted; identifying, using a processor, a window period within which the event is statistically correlated with the factor; collecting data for a duration of the window period, the data indicating occurrences of the factor and the event; and identifying a time lagged dependency of the event on the factor based on analyzing the data. 2. The method according to claim 1, wherein the identifying the window period includes analyzing historical information including times of occurrences of the event and times of occurrences of the factor for a time period between a first time of occurrence of the event and a last time of occurrence of the event among the times of occurrences of the event. 3. The method according to claim 2, wherein the analyzing the historical information includes computing a first average duration based on a duration between the times of occurrences of the event, except the first time of occurrence of the event, and the respective time of occurrence of the factor immediately preceding the respective time of occurrence of the event and also computing a second average duration based on a duration between the times of occurrences of the event, except the last time of occurrence of the event, and the respective time of occurrence of the factor immediately following the respective time of occurrence of the event. 4. The method according to claim 3, wherein the identifying the window period includes computing the window period based on the duration between the first time of occurrence of the event and the last time of occurrence of the event, the first average duration, and the second average duration. 5. The method according to claim 3, wherein the computing the first average duration is additionally based on a definitive closure time of the event preceding the respective event. 6. The method according to claim 1, further comprising generating summation data based on summing a number of occurrences of the factor over the window period prior to each event in the data. 7. The method according to claim 6, wherein the identifying the time lagged dependency is based on the summation data. 8. The method according to claim 7, wherein the identifying the time lagged dependency includes quantifying a correlation between the event and the factor based on the summation data. 9. A system to identify a time lagged indicator of an event to be predicted, the system comprising: an input interface configured to receive information including an indication of a factor, the factor being a different event than the event to be predicted; and a processor configured to identify a window period within which the event is statistically correlated with the factor, collect data for a duration of the window period, the data indicating occurrences of the factor and the event, and identify a time lagged dependency of the event on the factor based on analysis of the data. 10. The system according to claim 9, wherein the processor identifies the window period based on an analysis of historical information including times of occurrences of the event and times of occurrences of the factor for a time period between a first time of occurrence of the event and a last time of occurrence of the event among the times of occurrences of the event. 11. The system according to claim 10, wherein the processor computes a first average duration based on a duration between the times of occurrences of the event, except the first time of occurrence of the event, and the respective time of occurrence of the factor immediately preceding the respective time of occurrence of the event and also computes a second average duration based on a duration between the times of occurrences of the event, except the last time of occurrence of the event, and the respective time of occurrence of the factor immediately following the respective time of occurrence of the event. 12. The system according to claim 11, wherein the processor computes the window period based on the duration between the first time of occurrence of the event and the last time of occurrence of the event, the first average duration, and the second average duration. 13. The system according to claim 9, wherein the processor generates summation data based on summing a number of occurrences of the factor over the window period prior to each event in the data. 14. The system according to claim 13, wherein the processor identifies the time lagged dependency is based on the summation data. 15. The system according to claim 14, wherein the processor quantifies a correlation between the event and the factor based on the summation data. 16. A computer program product comprising instructions that, when processed by a processor, cause the processor to implement a method of identifying a time lagged indicator of an event to be predicted, the method comprising: receiving information including an indication of a factor, the factor being a different event than the event to be predicted; identifying a window period within which the event is statistically correlated with the factor; collecting data for a duration of the window period, the data indicating occurrences of the factor and the event; and identifying a time lagged dependency of the event on the factor based on analyzing the data. 17. The computer program product according to claim 16, wherein the identifying the window period includes analyzing historical information including times of occurrences of the event and times of occurrences of the factor for a time period between a first time of occurrence of the event and a last time of occurrence of the event among the times of occurrences of the event. 18. The computer program product according to claim 17, wherein the analyzing the historical information includes computing a first average duration based on a duration between the times of occurrences of the event, except the first time of occurrence of the event, and the respective time of occurrence of the factor immediately preceding the respective time of occurrence of the event and also computing a second average duration based on a duration between the times of occurrences of the event, except the last time of occurrence of the event, and the respective time of occurrence of the factor immediately following the respective time of occurrence of the event. 19. The computer program product according to claim 18, wherein the identifying the window period includes computing the window period based on the duration between the first time of occurrence of the event and the last time of occurrence of the event, the first average duration, and the second average duration. 20. The computer program product according to claim 16, further comprising generating summation data based on summing a number of occurrences of the factor over the window period prior to each event in the data, wherein the identifying the time lagged dependency includes quantifying a correlation between the event and the factor based on the summation data.