An online system stores objects representing potential transactions of an enterprise. The online system uses predictor models to determine an aggregate score based on values of the objects associated with a time interval, for example, a month. Each object is configured to take one of a plurality of states. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data for generating the predictor models. The online system categorizes the objects into bins based on states of the objects. The online system may generate different predictions for each category. The online system may use machine learning based models as predictor models. The online system extracts features describing potential transaction objects and provides these as input to the predictor model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: storing, by a computer system, a set of objects configured to perform state transitions responsive to changes in data associated with the object, each object associated with a value and having one of a plurality of states; storing a plurality of predictor models, each of the plurality of predictor models associated with a category and a position of a sub-interval in the sequence of sub-intervals, each predictor model determined based on historical data associated with the stored objects; identifying a set of objects from the plurality of objects; identifying a time interval comprising a sequence of sub-intervals and a current sub-interval from the sequence; for each of the set of objects, assigning the object to one of a plurality of categories based on a state of the object; for each of the plurality of categories: determining an aggregate object value for the objects assigned to the category; identifying a predictor model from the plurality of predictor models, the identified predictor model corresponding to the category and a position of the current sub-interval in the sequence; and determining an estimated category score for the category based on the identified predictor model and the aggregate object value; determining an estimated total score for the end of the time interval based on an aggregate category score for the plurality of categories; and sending the estimated total value for display by a user interface. 2. The method of claim 1, wherein each of the plurality of predictor models is a machine learning based linear regression model trained using historical data representing changes in data associated with objects. 3. The method of claim 1, wherein each object is represented as a database record. 4. The method of claim 1, wherein each category corresponds to one of the plurality of states of an object representing a potential transaction. 5. The method of claim 1, wherein a category corresponds to two or more states from the plurality of states of an opportunity object, the two or more states determined to be correlated based on a principal component analysis. 6. The method of claim 1, wherein the predictor model is selected from the plurality of predictor models based on an amount of time prior to the end of the time period. 7. The method of claim 4, wherein the predictor model is generated based on data associated with previous potential transactions at the amount of time prior to end of the corresponding previous time period. 8. The method of claim 1, wherein the predictor model is generated based on potential transactions of previous time periods and the aggregate category score at the end of each of the prior time periods. 9. The method of claim 1, wherein the time interval represents a month and each sub-interval represents a day of the month. 10. The method of claim 1, wherein historical data associated with the stored objects is stored in a database table, the method comprising: detecting a change in a value associated with an object representing a potential transaction; and adding a row to the database table responsive to detecting the change in the value. 11. The method of claim 1, wherein each object represents a potential transaction and the object score of the object represents a value of the potential transaction. 12. The method of claim 1, wherein identifying the set of objects from the plurality of objects comprises: determining whether a particular object is expected to reach a target state at the end of the time interval; and selecting the particular object for including in the set of objects responsive to the determination. 13. The method of claim 1, wherein the system is a multi-tenant system storing data for a plurality of tenants, each tenant representing an enterprise, wherein the plurality of linear regression models is for a particular tenant of the multi-tenant system. 14. A computer readable non-transitory storage medium storing instructions for: storing, by a computer system, a set of objects configured to perform state transitions responsive to changes in data associated with the object, each object associated with a value and having one of a plurality of states; storing a plurality of predictor models, each of the plurality of predictor models associated with a category and a position of a sub-interval in the sequence of sub-intervals, each predictor model determined based on historical data associated with the stored objects; identifying a set of objects from the plurality of objects; identifying a time interval comprising a sequence of sub-intervals and a current sub-interval from the sequence; for each of the set of objects, assigning the object to one of a plurality of categories based on a state of the object; for each of the plurality of categories: determining an aggregate object value for the objects assigned to the category; identifying a predictor model from the plurality of predictor models, the identified predictor model corresponding to the category and a position of the current sub-interval in the sequence; and determining an estimated category score for the category based on the identified predictor model and the aggregate object value; determining an estimated total score for the end of the time interval based on an aggregate category score for the plurality of categories; and sending the estimated total value for display by a user interface. 15. The computer readable non-transitory storage medium 14, wherein a category corresponds to two or more states from the plurality of states of an opportunity object, the two or more states determined to be correlated based on a principal component analysis. 16. The computer readable non-transitory storage medium 14, wherein the predictor model is selected from the plurality of predictor models based on an amount of time prior to the end of the time period. 17. The computer readable non-transitory storage medium 16, wherein the predictor model is generated based on data associated with previous potential transactions at the amount of time prior to end of the corresponding previous time period. 18. The computer readable non-transitory storage medium 14, wherein historical data associated with the stored objects is stored in a database table, the stored instructions further for: detecting a change in a value associated with an object representing a potential transaction; and adding a row to the database table responsive to detecting the change in the value. 19. The computer readable non-transitory storage medium 14, wherein the system is a multi-tenant system storing data for a plurality of tenants, each tenant representing an enterprise, wherein the plurality of linear regression models is for a particular tenant of the multi-tenant system. 20. A computer-implemented system comprising: a computer processor; and a computer readable non-transitory storage medium storing instructions thereon, the instructions when executed by a processor cause the processor to perform the steps of: storing, by a computer system, a set of objects configured to perform state transitions responsive to changes in data associated with the object, each object associated with a value and having one of a plurality of states; storing a plurality of predictor models, each of the plurality of predictor models associated with a category and a position of a sub-interval in the sequence of sub-intervals, each predictor model determined based on historical data associated with the stored objects; identifying a set of objects from the plurality of objects; identifying a time interval comprising a sequence of sub-intervals and a current sub-interval from the sequence; for each of the set of objects, assigning the object to one of a plurality of categories based on a state of the object; for each of the plurality of categories: determining an aggregate object value for the objects assigned to the category; identifying a predictor model from the plurality of predictor models, the identified predictor model corresponding to the category and a position of the current sub-interval in the sequence; and determining an estimated category score for the category based on the identified predictor model and the aggregate object value; determining an estimated total score for the end of the time interval based on an aggregate category score for the plurality of categories; and sending the estimated total value for display by a user interface.